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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
1. A fault detection method for a drainage pipe network, comprising:
acquiring a first liquid level time series collected at a current node in the drainage pipe network;
performing abnormality type identification according to the first liquid level time series to obtain an abnormal event type of the current node;
performing hydraulic characteristic matching on the first liquid level time series and a liquid level time series of a neighboring node of the current node by using a fault diagnosis algorithm corresponding to the abnormal event type to obtain a fault diagnosis result corresponding to the current node; the liquid level time series of the neighboring node comprising: a second liquid level time series corresponding to an upstream node of the current node, and/or a third liquid level time series corresponding to a downstream node of the current node.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly, there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The additional element of acquiring a first liquid level time series collected at a current node in the drainage pipe network is considered necessary data gathering and is not sufficient to integrate the abstract idea into a practical application. As recited in MPEP section 2106.05(g), necessary data gathering (i.e., receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
Claim 11 recites a memory, a processor, and a communication component. Claim 12 recites a non-transitory computer-readable medium and a computer program. These claims recite what is considered generic computer elements and not sufficient to integrate the abstract idea into a practical application.
Claims 2-10, 13-20 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-3, 10, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cheng et al. (CN 105065917 A) hereinafter Cheng
Regarding Claim 1, Cheng teaches acquiring a first liquid level time series collected at a current node in the drainage pipe network ([0022] “using level sensors to collect real-time pipeline level parameters h at important nodes, which mainly include pipeline intersections, pipeline diameter changes, pipeline slope changes, outlets, and common sewage discharge points”); performing abnormality type identification according to the first liquid level time series to obtain an abnormal event type of the current node ([0024] “the defect identification module in step S4 is used to identify the type of defect; the defect alarm threshold module is used to set alarm parameters; the defect treatment module is used to compare the pipeline liquid level parameter h, pipeline flow rate Q, and pipeline displacement parameter σ of the drainage network with the parameters set in the defect alarm threshold module, and to activate the corresponding alarm action according to the defect treatment method in the defect treatment module, so as to realize the analysis and early warning of defects such as urban flooding, pipeline congestion, pipeline subsidence, and illegal sewage discharge.”); performing hydraulic characteristic matching on the first liquid level time series and a liquid level time series of a neighboring node of the current node by using a fault diagnosis algorithm corresponding to the abnormal event type to obtain a fault diagnosis result corresponding to the current node ([0024] “the defect identification module in step S4 is used to identify the type of defect; the defect alarm threshold module is used to set alarm parameters; the defect treatment module is used to compare the pipeline liquid level parameter h, pipeline flow rate Q, and pipeline displacement parameter σ of the drainage network with the parameters set in the defect alarm threshold module, and to activate the corresponding alarm action according to the defect treatment method in the defect treatment module, so as to realize the analysis and early warning of defects such as urban flooding, pipeline congestion, pipeline subsidence, and illegal sewage discharge.”); the liquid level time series of the neighboring node comprising: a second liquid level time series corresponding to an upstream node of the current node, and/or a third liquid level time series corresponding to a downstream node of the current node ([0019] “Where n is the pipe roughness coefficient; A is the cross-sectional area of the water passage; R is the hydraulic radius; J is the hydraulic gradient;
h
j
and
h
j
+
1
are the measured upstream and downstream pipe level parameters of two adjacent monitoring points, i.e., the pipe level elevation; ΔL is the pipe length between two adjacent monitoring points; and j is the monitoring point number.”).
Regarding Claim 2, Cheng teaches the limitations of claim 1.
Cheng further teaches performing, if the fault diagnosis result indicates that there is a fault in a pipe between the current node and the downstream node, a fault assumption calculation on the pipe between the current node and the downstream node through a hydrodynamic model of the drainage pipe network to determine a faulty pipe between the current node and the downstream node ([0020] “S4: Establish an early warning system for the drainage network, setting up a defect identification module, a defect alarm threshold module, and a defect handling module. Combining the analysis results of the drainage network operation status and pipeline displacement status in step S3, the system makes real-time judgments on pipeline defects during the operation of the drainage network, enabling real-time early warning and handling.” Where S3 [0017-0019] discusses collecting the data from the upstream and downstream node and comparing it to the current node under test); sending information about the faulty pipe to a specified terminal device for fault prompting ([0021] “S5: Store the monitored operating status of the drainage network, pipeline displacement status, and information on defect treatment in the early warning database.” Where the data is sent to a computer starting in step 2 as explained in [0022] “establishing the on-site monitoring system for the drainage network in step S2 includes: … transmitting them to a computer,”).
Regarding Claim 3, Cheng teaches the limitations of claim 1.
Cheng further teaches performing inflection point detection on the first liquid level time series (Where it is well known in the art that the inflection point testing is used to locate the change in the slope/pattern of water flow or section of pipe area often used at pipe cross sections or changes in pipes, see [0022] “ using level sensors to collect real-time pipeline level parameters h at important nodes, which mainly include pipeline intersections, pipeline diameter changes, pipeline slope changes, outlets, and common sewage discharge points;”. With this context [0056] “the system collects and saves water flow level parameters and pipeline displacement parameters, calculates pipeline flow parameters through simulation models, and displays them in real time.” Calculating the pipe flow parameters is performing inflection point testing, for example [0057] “1. The downstream pipeline level
h
j
+
1
is consistently higher than the upstream pipeline level
h
j
, and the pipeline flow velocity
v
j
+
1
is less than
v
j
and
v
j
+
2
, indicating that there may be congestion between monitoring points j+1 and j+2 in the pipeline.” And that an inflection point is found.); determining the abnormal event type corresponding to the current node as a sudden pipe blockage type if an inflection point is detected from the first liquid level time series. ([0055] “Establish a drainage pipe network early warning system to make real-time judgments on pipe defects during the operation of the drainage pipe network based on the status analysis results, identify pipe blockage, waterlogging, and illegal sewage discharge, and analyze pipe settlement phenomena based on pipeline displacement status to achieve real-time early warning.”; for example [0057] “1. The downstream pipeline level
h
j
+
1
is consistently higher than the upstream pipeline level
h
j
, and the pipeline flow velocity
v
j
+
1
is less than
v
j
and
v
j
+
2
, indicating that there may be congestion between monitoring points j+1 and j+2 in the pipeline.”).
Regarding 10 and 20, Cheng teaches the limitations of claims 1 and 2, respectively.
performing trend comparison on the first liquid level time series and the third liquid level time series if the abnormal event type of the current node is a long-term blockage event ([0044] “several low-lying sections of the area have poor drainage during heavy rain and are prone to flooding (i.e., possible area of blockage)”; [0048] “level sensors are installed at inspection wells to monitor stormwater levels in low-lying areas and in the sewage pipes of the restaurant district. Real-time pipe level parameter h is collected, and the monitoring data is transmitted wirelessly to the drainage network status analysis system and stored in the early warning database.”); determining that the fault diagnosis result corresponding to the current node is that there is a long-term blockage fault in the pipe between the current node and the downstream node if a liquid level in the first liquid level time series presents a rising trend and a liquid level in the third liquid level time series has no rising trend ([0057] “1. The downstream pipeline level
h
j
+
1
is consistently higher than the upstream pipeline level
h
j
, and the pipeline flow velocity
v
j
+
1
is less than
v
j
and
v
j
+
2
, indicating that there may be congestion between monitoring points j+1 and j+2 in the pipeline.”, where [0053] “
h
j
and
h
j
+
1
are the upstream and downstream pipe level parameters of two adjacent monitoring points,” where one of ordinary skill in the art can apply the logic to each node being looked at and achieve the limitation of the pending application.).
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.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Kim et al. (KR 101105192 B1) hereinafter Kim.
Regarding Claim 4, Cheng teaches the limitation of claim 3.
Cheng does not teach dividing the first liquid level time series into a plurality of subsequences; calculating a loss function of the first liquid level time series and respective loss functions of the plurality of subsequences; calculating a signal difference between the plurality of subsequences according to a difference value between the loss function of the first liquid level time series and the respective loss functions of the plurality of subsequences; determining that there is an inflection point in the first liquid level time series if the signal difference of the subsequences is greater than a set penalty value.
Kim teaches dividing the first liquid level time series into a plurality of subsequences (Pg 7 paragraph 2; (a) a step in which a DB server receives, in a time-series manner, measurement data, namely flow rate, dynamic water pressure, and identification information of each flow rate meter and pressure gauge, from flow rate meters installed at a certain number of injection points of each sub-block in a water supply network and pressure gauges installed within the sub-blocks (i.e., subsequence locations);”; and [0032] “measurement points on a block system or water supply network optimal management system, which measure and transmit flow rate, water level, pressure, water quality, etc. at regular intervals (time series is split into intervals or subsequences)”)
calculating a loss function of the first liquid level time series and respective loss functions of the plurality of subsequences ([0032] “a water loss rate analysis server (900) that analyzes the water loss rate and leakage rate for each large/medium/small block of the water supply network may also be included”); calculating a signal difference between the plurality of subsequences according to a difference value between the loss function of the first liquid level time series and the respective loss functions of the plurality of subsequences (Pg 7 paragraph 2; “b) a step in which a network analysis server compares and analyzes the flow rate information received from the flow rate meters at the injection points of each sub-block in step (a) to select a sub-block exhibiting an abnormal outflow pattern; (c) a step in which the network analysis server adjusts the calculated value of each dynamic water pressure at the same node based on normal network analysis using the flow rate (Q) fluctuation, based on the actual measured value of dynamic water pressure measured by each pressure gauge for the sub-block selected in step;”); determining that there is an inflection point in the first liquid level time series if the signal difference of the subsequences is greater than a set penalty value (Pg 7 paragraph 2; (d) a step in which the network analysis server sorts the nodes in order of the magnitude of the flow rate (Q) fluctuation in step (c) and determines that the nodes in order of the magnitude of the flow rate (Q) fluctuation are nodes in the section suspected of leakage.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the division of level time series as taught by Kim to the fault drain detection for the purpose of splitting the time series into smaller blocks data from individual nodes. This is advantageous because it allows the data model to process and articulate where the abnormal data is being received from .
Claim(s) 6-9, 13-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Cheng and Kim and further in view of Peleg et al (CN 102884407 A) hereinafter Peleg
Regarding Claim 5, Cheng teaches the limitations of claim 1.
Cheng further teaches determining the abnormal event type of the current node as a blockage type if the liquid level trend of the current node presents a continuous rising trend ([0057] “1. The downstream pipeline level
h
j
+
1
is consistently higher than the upstream pipeline level
h
j
, and the pipeline flow velocity
v
j
+
1
is less than
v
j
and
v
j
+
2
, indicating that there may be congestion between monitoring points j+1 and j+2 in the pipeline.”, where [0053] “
h
j
and
h
j
+
1
are the upstream and downstream pipe level parameters of two adjacent monitoring points,” where one of ordinary skill in the art can apply the logic to each node being looked at and achieve the limitation of the pending application.).
Cheng does not teach performing time series decomposition on the first liquid level time series to obtain a liquid level trend of the current node; where the blockage can be identified as long-term blockage.
Kim further teaches performing time series decomposition on the first liquid level time series to obtain a liquid level trend of the current node ([0039]"compares and analyzes time-based inflow information at the injection point of the sub-block to select a sub-block that exhibits an abnormal outflow pattern (cases where there is outflow other than the planned water volume due to leakage or other abnormal outflow, i.e., cases where the flow rate at the injection point rises rapidly compared to normal times and the rapidly rising flow rate is maintained).").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the division of level time series as taught by Kim to the fault drain detection for the purpose of splitting the time series into smaller blocks data from individual nodes. This is advantageous because it allows the data model to process and articulate where the abnormal data is being received from .
Cheng and Kim do not teach where the blockage can be identified as long-term blockage.
Peleg teaches where the blockage can be identified as long-term blockage ([0048] “Anomaly detector 206 analyzes the significance of deviations over a period of time (e.g., after several minutes, several hours, several days, or longer), because, for example, continuous or frequent deviations increase the significance of said deviations. Those skilled in the art will recognize that system designers will design or adjust anomaly detector 206 to analyze deviations over a period of time based on required sensitivity, etc. For events on smaller time scales, or recently started events, they are typically detectable when they have a large magnitude, while for small-magnitude events, continuous deviations over a longer period of time are required to detect the event. Therefore, small deviations that occur only once or only within a very short period of time, such as one minute, will not be detected as abnormal, while the same small deviations that occur frequently over a longer period of time will be identified as statistically significant by the anomaly detector 206 and detected as abnormal.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine determining a long term blockage found in Peleg to the fault detection method of a drainage pipe discussed in Cheng and Kim for the purpose of being able to determine the length of time that a pipe has been affected. This is advantageous because it allows for historical anomaly detection by use of monitoring the if the frequency of occurrence has exceeded what would be considered a normal amount (threshold) within a certain time frame (e.g., [0019] , Peleg).
Regarding Claim 6, Cheng, Kim and Peleg teach the limitations of claim 5.
Cheng does not teach determining a blockage level corresponding to the current node according to a ratio of an amount of change in the rising trend of a liquid level of the current node to a pipe diameter.
Kim teaches determining a blockage level corresponding to the current node according to a ratio of an amount of change in the rising trend of a liquid level of the current node to a pipe diameter ([ ] paragraph 2 “(c) involves the pipe network analysis server using a program within the server that includes the following Hazen- Williams equation (where HL is the frictional head loss (m) (i.e., change in rising trend of liquid), L is the length of the pipe (m), D is the pipe diameter (m), … When the flow rate (Q) at each node where a pressure gauge is installed within the sub-block selected in step (b) is varied, the velocity head and frictional head loss affected by the flow rate (Q) at that node are.”; where [0043] “the node with the most severe fluctuation in the parameter flow rate (Q) can be identified, and it can be determined that the area near this node is a section where leakage or/or other abnormal outflow occurs.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the ratio of amount rising as taught by Kim to the fault drain detection for the purpose of splitting the time series into smaller blocks data from individual nodes. This is advantageous because it allows the data model to process and articulate where the abnormal data is being received from using specific ratios and numerical results from the pipe dimensions.
Regarding Claim 7, Cheng teaches the limitations of claim 1.
Cheng further teaches calculating, according to an extracted feature, a probability that a pipe between the current node and the downstream node belongs to at least one abnormal event type; the at least one abnormal event type comprising at least one of: a sudden blockage event and long-term blockage events of different levels ([0056] “calculates pipeline flow parameters through simulation models, and displays them in real time. On the other hand, it establishes an early warning database. Management personnel can search the database based on pipeline ID, parameter type, installation location, collection time, pipeline diameter, and water usage characteristics. Using a defect identification module, by comparing the pipeline's operating status with the simulated operating status of various network defects in the early warning system, phenomena such as pipeline settlement (i.e., long term) and illegal sewage discharge (i.e., short term) can be identified.” And [0061] “Therefore, an alarm threshold module is used to set alarm thresholds for various parameters to assess the pipeline network's operational status. The alarm thresholds set in the defect handling module are divided into first-level, second-level, and third-level alarm thresholds.”); outputting the abnormal event type of the current node according to the probability that the pipe between the current node and the downstream node belongs to the at least one abnormal event type ([0057] “1. The downstream pipeline level
h
j
+
1
is consistently higher than the upstream pipeline level
h
j
,
and the pipeline flow velocity
V
j
+
1
is less than
v
j
and
v
j
+
2
,indicating that there may be congestion between monitoring points j+1 and j+2 in the pipeline.” Where [0061] “The early warning information not only includes the location and severity of the defect, but also provides pre-stored corresponding defect resolution suggestions. Therefore, an alarm threshold module is used to set alarm thresholds for various parameters to assess the pipeline network's operational status.”)
Cheng does not teach inputting the first liquid level time series and the third liquid level time series into a computer system; performing feature extraction on the first liquid level time series and the third liquid level time series; deep learning model.
Kim teaches inputting the first liquid level time series and the third liquid level time series into a computer system; ([ ] paragraph 2 “(a) a step in which a DB server receives, in a time-series manner, measurement data, namely flow rate, dynamic water pressure, and identification information of each flow rate meter and pressure gauge, from flow rate meters installed at a certain number of injection points of each sub-block in a water supply network and pressure gauges installed within the sub-blocks;”); performing feature extraction on the first liquid level time series and the third liquid level time series ([ ] paragraph 2; (b) a step in which a network analysis server compares and analyzes the flow rate information received from the flow rate meters at the injection points of each sub-block in step (a) to select a sub-block exhibiting an abnormal outflow pattern;”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the division of level time series as taught by Kim to the fault drain detection for the purpose of splitting the time series into smaller blocks data from individual nodes. This is advantageous because it allows the data model to process and articulate where the abnormal data is being received from .
Cheng and Kim do not teach a deep learning model.
Peleg teaches deep learning model ([0046] “predictors can be designed using machine learning frameworks to perform statistical analysis on data. Examples of machine learning frameworks are discussed in Ethem Alpaydin, Introduction to Machine Learning”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine a deep learning model as found in Peleg to the fault detection method of a drainage pipe discussed in Cheng and Kim for the purpose of being able to sort and analyze large sets of data from the pipe network. This is advantageous because it allows for the determines the statistical probability that no relevant anomaly occurred for a given meter reading within a given time period by analyzing the significance of the deviation of large data sets where data is retrieved from multiple devices/locations (e.g., [0021, 0048] , Peleg).
Regarding Claim 8, Cheng, Kim and Peleg teach the limitations of claim 7.
Cheng further teaches the liquid level sequence sample comprising a plurality of sets of liquid level trend comparison data of neighboring upstream and downstream nodes([0057] “1. The downstream pipeline level
h
j
+
1
is consistently higher than the upstream pipeline level
h
j
,
and the pipeline flow velocity
V
j
+
1
is less than
v
j
and
v
j
+
2
,indicating that there may be congestion between monitoring points j+1 and j+2 in the pipeline.”); the liquid level sequence sample being acquired by monitoring liquid level data of the drainage pipe network, and/or being obtained through simulation of a hydrodynamic model of the drainage pipe network ([0056] “The drainage network status analysis system collects data from the on-site monitoring system in real time. On one hand, the system collects and saves water flow level parameters and pipeline displacement parameters, calculates pipeline flow parameters through simulation models, and displays them in real time. On the other hand, it establishes an early warning database.”); performing feature extraction on the liquid level sequence sample to obtain a sample feature [0057] “1. The downstream pipeline level
h
j
+
1
is consistently higher than the upstream pipeline level
h
j
, and the pipeline flow velocity
v
j
+
1
is less than
v
j
and
v
j
+
2
, indicating that there may be congestion between monitoring points j+1 and j+2 in the pipeline.” A sample feature is recorded.).
Cheng and Kim do not teach acquiring a liquid level sequence sample marked with an abnormality type true value; performing abnormality prediction according to the sample feature and a parameter of the deep learning model to obtain an abnormality type prediction result corresponding to the liquid level sequence sample; training the deep learning model according to an error between the abnormality type prediction result and the abnormality type true value marked on the liquid level sequence sample until the error converges to a specified range
Peleg teaches acquiring a liquid level sequence sample marked with an abnormality type true value ([0047] “As shown in Figure 5, the dataset received from predictor 205 includes distributions with possible values, variance, and any other statistical descriptors about the values. Those skilled in the art will recognize that a dataset can contain multiple possible and actual values (i.e., abnormality type true value) for the instrument being analyzed.”); performing abnormality prediction according to the sample feature and a parameter of the deep learning model to obtain an abnormality type prediction result corresponding to the liquid level sequence sample ([0048] “For each dataset, each anomaly detector determines the statistical probability that no relevant anomaly occurred for a given meter reading within a given time period by analyzing the significance of the deviation. Anomaly detector 206 analyzes the significance of deviations over a period of time (e.g., after several minutes, several hours, several days, or longer), because, for example, continuous or frequent deviations increase the significance of said deviations. Those skilled in the art will recognize that system designers will design or adjust anomaly detector 206 to analyze deviations over a period of time based on required sensitivity, etc.”); training the deep learning model according to an error between the abnormality type prediction result and the abnormality type true value marked on the liquid level sequence sample until the error converges to a specified range ([0052] “Engine 207 will increase the statistical probability of an event based on the detection of multiple anomalies from the same or different instruments and at the same time or within a given period of time, all of which consistently indicate the occurrence of the event. For example, one exception may indicate the start of an event, another exception may indicate a change in the event or the end of the event, and the classification engine 207 will identify those exceptions as being related to a single event. As another example, two anomalies from different meters at similar times and originating from relevant locations, related to an increase in traffic, will both indicate the same event. In one embodiment, the trial-and-error method is used to determine the overall statistical probability of an instrument reading based on a combination of the statistical probability of readings based on time statistics and the statistical probability of readings based on spatial statistics. For example, if there is only a 15% probability that the current reading of a historical statistical data comparison indicator is that high, but there is a 95% probability that the current reading of a spatial statistical data comparison indicator is that high, then the overall reading is likely to be 75% likely to be that high.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine a deep learning model as found in Peleg to the fault detection method of a drainage pipe discussed in Cheng and Kim for the purpose of being able to sort and analyze large sets of data from the pipe network. This is advantageous because it allows for the determines the statistical probability that no relevant anomaly occurred for a given meter reading within a given time period by analyzing the significance of the deviation of large data sets where data is retrieved from multiple devices/locations (e.g., [0021, 0048] , Peleg).
Regarding Claims 9, 13, 14, 15, 16, 17, 18, and 19, Cheng teaches the limitations of 1, 2, 3 ; Cheng and Kim teach the limitations of 4; and Cheng, Kim and Peleg teach the limitations of 5, 6, 7, and 8 respectively.
Cheng teaches determining that the fault diagnosis result corresponding to the current node is that there is a sudden blockage fault in the pipe between the current node and the downstream node ([0057] “1. The downstream pipeline level
h
j
+
1
is consistently higher than the upstream pipeline level
h
j
, and the pipeline flow velocity
v
j
+
1
is less than
v
j
and
v
j
+
2
, indicating that there may be congestion between monitoring points j+1 and j+2 in the pipeline.”, where [0053] “
h
j
and
h
j
+
1
are the upstream and downstream pipe level parameters of two adjacent monitoring points,” where one of ordinary skill in the art can apply the logic to each node being looked at and achieve the limitation of the pending application.), comparing whether an appearance moment of the inflection point of the second liquid level time series is later than the appearance moment of the inflection point of the first liquid level time series event ([0056] “the system collects and saves water flow level parameters and pipeline displacement parameters, calculates pipeline flow parameters through simulation models, … by comparing the pipeline's operating status with the simulated operating status of various network defects in the early warning system, phenomena such as pipeline settlement and illegal sewage discharge can be identified.” Where as an example [0057] “1. The downstream pipeline level
h
j
+
1
is consistently higher than the upstream pipeline level
h
j
, and the pipeline flow velocity
v
j
+
1
is less than
v
j
and
v
j
+
2
, indicating that there may be congestion between monitoring points j+1 and j+2 in the pipeline.”), determining that the fault diagnosis result corresponding to the current node is that there is a sudden blockage fault in the pipe between the current node and the downstream node ([0057] “1. The downstream pipeline level
h
j
+
1
is consistently higher than the upstream pipeline level
h
j
, and the pipeline flow velocity
v
j
+
1
is less than
v
j
and
v
j
+
2
, indicating that there may be congestion between monitoring points j+1 and j+2 in the pipeline.”).
Cheng and Kim do not teach determining whether there is an inflection point in the second liquid level time series and the third liquid level time series if the abnormal event type of the current node is a sudden blockage event; if there is no inflection point in the second liquid level time series and the third liquid level time series; if there is an inflection point in the second liquid level time series and there is no inflection point in the third liquid level time series; if the appearance moment of the inflection point in the second liquid level time series is later than the appearance moment of the inflection point in the first liquid level time series.
Peleg teaches determining whether there is an inflection point in the second liquid level time series and the third liquid level time series if the abnormal event type of the current node is a sudden blockage event ([0048] “For events on smaller time scales, or recently started events (i.e., sudden blockage), they are typically detectable when they have a large magnitude, while for small-magnitude events, continuous deviations over a longer period of time are required to detect the event. Therefore, small deviations that occur only once or only within a very short period of time, such as one minute, will not be detected as abnormal, while the same small deviations that occur frequently over a longer period of time will be identified as statistically significant by the anomaly detector 206 and detected as abnormal.”); if there is no inflection point in the second liquid level time series and the third liquid level time series ([0048] “Therefore, small deviations that occur only once or only within a very short period of time, such as one minute, will not be detected as abnormal,”); if there is an inflection point in the second liquid level time series and there is no inflection point in the third liquid level time series ([0048] “Therefore, small deviations that occur only once or only within a very short period of time, such as one minute, will not be detected as abnormal,”); if the appearance moment of the inflection point in the second liquid level time series is later than the appearance moment of the inflection point in the first liquid level time series ([0048] “Therefore, small deviations that occur only once or only within a very short period of time, such as one minute, will not be detected as abnormal, while the same small deviations that occur frequently over a longer period of time will be identified as statistically significant by the anomaly detector 206 and detected as abnormal.” Where the detection of multiple inflection points would indicate abnormal).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine determining the duration of a blockage found in Peleg to the fault detection method of a drainage pipe discussed in Cheng and Kim for the purpose of being able to determine the length of time that a pipe has been affected. This is advantageous because it allows for historical anomaly detection by use of monitoring the if the frequency of occurrence has exceeded what would be considered a normal amount (threshold) within a certain time frame (e.g., [0019] , Peleg).
Regarding Claim 11, Cheng teaches the limitations of claim 1.
Cheng and Kim do not teach the memory being configured for storing one or more computer instructions; the processor being configured for executing the one or more computer instructions to execute steps.
Peleg teaches the memory being configured for storing one or more computer instructions ([0116] “The computer program (also referred to as computer control logic or computer-readable program code) is stored in main and/or secondary memory and executed by one or more processors (controllers, etc.)”); the processor being configured for executing the one or more computer instructions to execute steps ([0116] “The computer program (also referred to as computer control logic or computer-readable program code) is stored in main and/or secondary memory and executed by one or more processors (controllers, etc.)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine a processors and memory as found in Peleg to the fault detection method of a drainage pipe discussed in Cheng and Kim for the purpose of being able to sort and analyze large sets of data from the pipe network. This is advantageous because it allows for machine learning to determines the statistical probability that no relevant anomaly occurred for a given meter reading within a given time period by analyzing the significance of the deviation (e.g., [0048] , Peleg).
Regarding Claim 12, Cheng teaches the limitations of claim 1.
Cheng and Kim do not teach a non-transitory computer-readable storage medium stored with a computer program which, when executed, can implement steps.
Peleg teaches a non-transitory computer-readable storage medium stored with a computer program which, when executed, can implement steps ([0116] “The computer program (also referred to as computer control logic or computer-readable program code) is stored in main and/or secondary memory and executed by one or more processors (controllers, etc.)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine a processors and memory as found in Peleg to the fault detection method of a drainage pipe discussed in Cheng and Kim for the purpose of being able to sort and analyze large sets of data from the pipe network. This is advantageous because it allows for machine learning to determines the statistical probability that no relevant anomaly occurred for a given meter reading within a given time period by analyzing the significance of the deviation (e.g., [0048] , Peleg).
Conclusion
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/EMMA ALEXANDER/Patent Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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