DETAILED ACTOIN
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 09/14/2023.
Claims 1 – 20 are presented for examination.
Information Disclosure Statement
IDS dated 09/14/2023 has been reviewed. See attached.
Drawings
The drawing dated 09/14/2023 has been reviewed. They are accepted.
Specification
The disclosure is objected to because of the following informalities:
• In paragraph 0029, line 2, “… flows4 …” should read “… flow [4] …”. line 4, “…dynamics26,27…” should read “…dynamics [26],[27] …”.
Appropriate correction is required.
Claim Objections
Claim 4 and 15 objected to because of the following informalities:
• In claim 4, line 1, “… generating using the computing device to generate an alert …” should read “… generating, using the computing device, an alert …”.
• In claim 15, line 2, “… the set of instructions are …” should read “… the set of instructions is …”. Line 2, “ … in advanced …” should read “… in advance …”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Claim 1.
STEP 1. Yes. The claim recites: “A data-driven method”.
STEP 2A, PRONG ONE: Yes. The claim recites:
for flood crest characteristics forecasting, the method comprising:
measuring index velocity at an index velocity gaging station to provide index velocity data;
simultaneously with measuring the index velocity, measuring water stage at the index velocity gaging station to provide stage data; and
applying a data-driven model implemented using a computing device to the index velocity data and the stage data,
the data-driven model further uses historical index velocity data and historical stage data collected at the index velocity gaging station to provide a forecast of at least one of: the magnitude of a flood crest arrival during the occurrence of a hydrological event and the timing of the flood crest arrival during the occurrence of the hydrological event.
which recites a mathematical concept in the form of a data-driven model that processes measured index velocity data, water stage data, historical index velocity data, and historical water stage data to forecast the magnitude and timing of a flood crest. Mathematical models used to analyze data and generate predictions constitute abstract ideas.
STEP 2A, PRONG TWO: No. The additional elements measuring index velocity, measuring water stage at an index velocity gating station, implementing the data-driven model using a computing device merely collect data for use by the mathematical model and execute the model on a generic computer, and using historical index velocity data and historical stage data. These elements do not improve the functioning of the computer or other technologies. Instead, the claim merely produces a forecast. Therefore does not integrate the judicial exception into a practical application. See MPEP 2106.04(d) referencing MPEP 2016.05(f)) – “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more”.
STEP 2B: No. The additional elements, considered individually and as an ordered combination, do not provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. The measuring steps merely obtain data that serves as inputs to the mathematical model, and the computing device merely performs the calculation represented by the data-driven model and outputs the resulting forecast. These additional elements do not impose any meaningful limitation on the judicial exception. The claim does not include significantly more than the abstract idea.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 2.
Claim 2 is rejected under 35 U.S.C. 101 for the same reasons set forth with respect to claim 1.
The claim recites:
The data-driven method of claim 1 wherein the index velocity data and the stage data are concurrently collected using a Horizontal Acoustic Doppler Current Profiler (HADCP).
STEP 2A, PRONG ONE: Yes. These limitations continue to recite a mathematical concept (the data-driven model) for forecasting the magnitude and timing of the flood crest using measured and historical data, which is an abstract idea.
STEP 2A, PRONG TWO: No. The additional elements wherein the index velocity data and the stage data are concurrently collected using a Horizontal Acoustic Doppler Current Profiler (HADCP). This limitation merely specifies a particular instrument for collecting data that is subsequently processed by the mathematical model. The claim does not recite any improvement to the operation of the HADCP or to another technology or technical field. Instead, the HADCP performs its ordinary function of measuring hydrological parameters. Accordingly, the additional limitation does not integrate the judicial exception into a practical application.
STEP 2B: No. The additional limitation of concurrently collecting the index velocity data and the stage data using a Horizontal Acoustic Doppler Current Profiler (HADCP) merely identifies the source of the input data supplied to the mathematical model. The HADCP performs its ordinary function of measuring hydrological parameters, and the computing device merely executes the mathematical model to generate the forecast. Considered individually and as an ordered combination with the remaining claim elements, these additional elements do not impose a meaningful limitation on the judicial exception or provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Accordingly, the claim does not recite significantly more than the mathematical concept itself.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 3.
Claim 3 is rejected under 35 U.S.C 101 because it depends on claim 2. Claim 3 recites:
The data-driven method of claim 2 wherein the applying the data-driven model uses magnitude of the stage data and the index velocity data at onset of an index velocity pulse, rates of change for the index velocity and stage associated with the index velocity pulse, duration of rising of the index velocity pulse to an index velocity peak, an unsteadiness coefficient, and a time interval between the index velocity peak and an associated stage peak.
STEP 2A, PRONG ONE: Yes. Claim 3 still recites the same judicial exception – a mathematical concept. The additional limitations merely specify which variables are used by the data-driven model, including: magnitude of the stage data, the index velocity data at onset of an index velocity pulse, rates of change for the index velocity, stage associated with the index velocity pulse, duration of rising of the index velocity pulse to an index velocity peak, an unsteadiness coefficient, and a time interval between the index velocity peak and an associated stage peak. These are mathematical inputs or parameters used by the forecasting model. Adding more variables to a mathematical model does not change the fact that the claim is directed to a mathematical concept.
STEP 2A, PRONG TWO: No. The additional limitation is simply that the data-driven model uses the above parameters. These limitations do not: improve the HADCP; improve the gaging station; improve the computing device; improve a measurement technique. Instead, they merely define additional inputs to the mathematical model. Accordingly, the judicial exception is not integrated into a practical application.
STEP 2B: No. The additional elements do not provide an inventive concept. The claim merely specifies that the mathematical model uses particular variables. Those limitations describe the content of the mathematical analysis, not an improvement in computer technology or measurement technology. Specifying additional mathematical variables for use in a mathematical model does not amount to significantly more than the judicial exception.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 4.
The claim recites: The data-driven method of claim 1 further comprising generating using the computing device to generate an alert in advance of flood crest based on the forecast of at least one of the magnitude of the flood crest arrival and the timing of the flood crest arrival.
STEP 2A, PRONG ONE: Yes. Claim 4 depends from claim 1, which recites a judicial exception in the form of a mathematical concept. Claim 4 further recites generating, using the computing device, an alert in advance of flood crest based on the forecast, which is an additional limitation beyond the judicial exception.
STEP 2A, PRONG TWO: No. The additional limitation of generating an alert in advance of the flood crest based on the forecast does not integrate the judicial exception into a practical application. Specifically, generating the alert merely uses the result of the mathematical concept to provide a notification. The claim does not recite improving the operation of the computer device, the water gaging station, or any other flood monitoring equipment. Nor does the claim recite controlling a physical device or applying the forecast to effect a practical application. Accordingly, the additional limitation constitutes insignificant extra-solution activity and does not integrate the judicial exception into a practical application.
STEP 2B: No. The additional limitation of generating an alert using a computing device based on the forecast merely outputs information resulting from the mathematical concept. Considered individually and as an ordered combination with the remaining claim elements, this additional limitation constitutes insignificant extra-solution activity and does not amount to significantly more than the judicial exception.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 5.
The claim recites:
wherein the forecast includes both the magnitude of the flood crest arrival and the timing of the flood crest arrival.
STEP 2A, PRONG ONE: Yes. Claim 5 recites a judicial exception because it depends from claim 1, which recites a mathematical concept. The additional limitation that the forecast includes both the magnitude of the flood crest arrival and the timing of the flood crest arrival merely further defines the output of the recited mathematical model. It does not add a limitation beyond the judicial exception.
STEP 2A, PRONG TWO: No. The claim does not recite any additional elements beyond those discussed with respect to claim 1 that integrate the judicial exception into a practical application. The additional limitation merely specifies that the mathematical model forecasts both the magnitude and timing of the flood crest. Accordingly, claim 5 does not integrate the judicial exception into a practical application.
STEP 2B: No. The limitation that the forecast includes both the magnitude of the flood crest arrival and the timing of the flood crest arrival merely further defines the mathematical concept recited in claim 1 and does not add any additional element that amounts to significantly more than the judicial exception. Accordingly, the claim does not recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 6.
Claim 6 recites:
comprising acquiring the historical index velocity data and the historical stage data to use in the data-driven model.
STEP 2A, PRONG ONE: Yes. Claim 6 recites a judicial exception because it depends from claim 1, which recites a mathematical concept. Specifically, claim 1 recites applying a data-driven model to measured and historical data to forecast at least one of the magnitude and timing of a flood crest. Claim 6 further recites additional elements for acquiring the historical index velocity data and the historical stage data for use in the data-driven model.
STEP 2A, PRONG TWO: No. The additional limitation of acquiring the historical index velocity data and the historical stage data merely gathers data for use by the recited mathematical concept. The claim does not recite any improvements to the acquisition of the historical data, the computing device, the water gaging station, or any other technologies. Accordingly, the additional limitation constitutes data gathering in preparation for performing the mathematical concept and does not integrate the judicial exception into a practical application.
STEP 2B: No. The additional limitation of acquiring the historical index velocity data and the historical stage data merely gathers data for use by the recited mathematical concept. Considered individually and as an ordered combination with the remaining claim elements, the additional limitation constitutes insignificant extra-solution activity and does not amount to significantly more than the judicial exception.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 7.
Claim 7 recites:
wherein the hydrological event is a single pulse storm.
STEP 2A, PRONG ONE: Yes. Claim 7 recites a judicial exception because it depends from claim 1, which recites a mathematical concept. Claim 7 further limits the hydrological event to a single pulse storm.
STEP 2A, PRONG TWO: No. The additional limitation that the hydrological event is a single pulse storm merely limits the judicial exception to a particular environment or field of use. The claim does not recite any improvement to the computing device, the water gaging station, or any other technologies. Nor does it apply the mathematical concept in a manner that imposes a meaningful limit on the judicial exception. Accordingly, the claim does not integrate the judicial exception into a practical application.
STEP 2B: No. The additional limitation that the hydrological event is a single pulse storm merely limits the application of the judicial exception to a particular type of hydrological event and does not add any element that amounts to significantly more than the judicial exception. Accordingly, the claim does not recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 8.
The claim recites:
wherein the hydrological event is a multi-pulse storm.
STEP 2A, PRONG ONE: Yes. Claim 8 recites a judicial exception because it depends from claim 1, which recites a mathematical concept. Claim 8 further limits the hydrological event to a multi-pulse storm.
STEP 2A, PRONG TWO: No. The additional limitation that the hydrological event is a multi-pulse storm merely limits the judicial exception to a particular environment or field of use. The claim does not recite any improvement to the computing device, the water gaging station, or any other technologies. Nor does it apply the mathematical concept in a manner that imposes a meaningful limit on the judicial exception. Accordingly, the claim does not integrate the judicial exception into a practical application.
STEP 2B: No. The additional limitation that the hydrological event is a multi-pulse storm merely limits the application of the judicial exception to a particular type of hydrological event and does not add any element that amounts to significantly more than the judicial exception. Accordingly, the claim does not recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 9.
Claim 9 recites:
comprising calibrating a predictive channel routing numerical model using the forecast.
STEP 2A, PRONG ONE: Yes. Claim 9 recites a judicial exception because it depends from claim 1, which recites a mathematical concept. Claim 9 further recites calibrating a predictive channel routing numerical model using the forecast, which is an additional element.
STEP 2A, PRONG TWO: No. The additional limitation of calibrating a predictive channel routing numerical model using the forecast does not integrate the judicial exception into a practical application. The claim does not recite improving the operation of the computing device, or any other technology. Rather, the forecast generated by the mathematical concept is used as an input for calibrating another numerical model. Accordingly, the additional limitation merely uses the output of one mathematical model as an input to another numerical model, and does not impose a meaningful limit on the judicial exception.
STEP 2B: No. The additional limitation of calibrating a predictive channel routing numerical model using the forecast merely applies the output of the judicial exception in another numerical model and does not add any additional element that amounts to significantly more than the judicial exception. Considered individually and as an ordered combination with the remaining claim elements, the claim does not recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 10.
Claim 10 recites:
comprising validating a predictive channel routing numerical model using the forecast.
STEP 2A, PRONG ONE: Yes. Claim 10 recites a judicial exception because it depends from claim 1, which recites a mathematical concept. Claim 10 further recites validating a predictive channel routing numerical model using the forecast, which is an additional element.
STEP 2A, PRONG TWO: No. The additional limitation of validating a predictive channel routing numerical model using the forecast does not integrate the judicial exception into a practical application. The claim does not recite improving the operation of the computing device, the predictive channel routing numerical model, or any other technologies. Rather, the forecast generated by the mathematical concept is used in the validation of another numerical model. Accordingly, the additional limitation merely uses the output of the mathematical concept in another numerical analysis and does not impose a meaningful limit on the judicial exception.
STEP 2B: No. The additional limitation of validating a predictive channel routing numerical model using the forecast merely uses the output of the judicial exception in the validation of another numerical model and does not add any additional element that amounts to significantly more than the judicial exception. Considered individually and as an ordered combination with the remaining claim elements, the claim does not recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 11.
Claim recites:
wherein the data-driven model is implemented using a set of instructions and regression lines determined from historical data stored in a machine readable non-transitory medium of the computing device and executed by at least one processor of the computing device.
STEP 2A, PRONG ONE: Yes. Claim 11 recites a judicial exception because it depends from claim 1, which recites a mathematical concept. Claim 11 further specifies that the data-driven model is implemented using a set of instructions and regression lines determined from historical data stored in a machine-readable non-transitory medium and executed by at least one processor of the computing device. The recitation of regression lines further defines the mathematical concept recited in claim 1.
STEP 2A, PRONG TWO: No. The additional limitations that the data-driven model is implemented using a set of instructions … stored in a machine-readable non-transitory medium and executed by at least one processor do not recite any improvement to the operation of the processor, the computing device, the machine-readable medium, or any other technologies. Rather, the additional limitations merely implement the mathematical concept using generic computer components performing their ordinary functions.
STEP 2B: No. The additional limitations of storing the set of instructions and regression lines determined from historical data in a machine-readable non-transitory medium and executing the set of instructions using at least one processor merely implement the mathematical concept on generic computer components. The machine-readable non-transitory medium merely stores the instructions and associated data, and the processor merely executes the instructions to perform the mathematical model. Considered individually and as an ordered combination with the remaining claim elements, these additional limitations do not impose a meaningful limitation on the judicial exception or provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Accordingly, the claim does not recite significantly more than the mathematical concept itself.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 12.
STEP 1. Yes. The claim recites: “A system”.
for implementing a data-driven method for streamflow forecasting, the system comprising:
a memory;
a processor operatively connected to the memory; and
a set of instructions stored on the memory for execution by the processor wherein the set of instructions are configured to apply a data-driven model to index velocity data and stage data, the data-driven model further using historical index velocity data and historical stage data to forecast at least one of a magnitude of a flood crest arrival and a timing of a flood crest arrival during occurrence of a hydrological event.
STEP 2A, PRONG ONE: Yes. This claim recites a judicial exception. Specifically, the claim recites applying a data-driven model to index velocity data, stage data, historical index velocity data, and historical stage data to forecast at least one of the magnitude of a flood crest arrival and a timing of a flood crest arrival during occurrence of a hydrological event. The recited data-driven model constitutes a mathematical concept and, therefore, an abstract idea.
STEP 2A, PRONG TWO: No. The additional limitations of a memory, a processor operatively connected to the memory, and a set of instructions stored on the memory for execution by the processor do not integrate the judicial exception into a practical application. The claim does not recite any improvement to the operation of the memory, the processor, or any other technologies. Rather, the additional limitations merely implement the recited mathematical concept using generic computer components performing their ordinary functions.
STEP 2B: No. The additional limitations of a memory, a processor operatively connected to the memory, and a set of instructions stored on the memory for execution by the processor merely implement the mathematical concept using generic computer components. The memory merely stores the instructions and associated data, and the processor merely executes the instructions to apply the data-driven model for forecasting. Considered individually and as an ordered combination with the remaining claim elements, these additional limitations do not impose a meaningful limitation on the judicial exception or provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Accordingly, the claim does not recite significantly more than the mathematical concept itself.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 13.
STEP 1. Yes. The claim recites “The system”. The claim continues to recite:
further comprising an index velocity measuring device for measuring the index velocity data and a water stage measuring device for measuring the stage data concurrently with the measuring of the index velocity data.
STEP 2A, PRONG ONE: Yes. Claim 13 recites a judicial exception because it depends from claim 12, which recites applying a data-driven model to index velocity data, stage data, historical index velocity data, and historical stage data to forecast at least one of a magnitude of a flood crest arrival and a timing of a flood crest arrival during occurrence of a hydrological event. The recited data-driven model constitutes a mathematical concept and, therefore, an abstract idea. Claim 13 further recites an index velocity measuring device and a water stage measuring device for concurrently measuring the index velocity data and stage data, which are additional elements.
STEP 2A, PRONG TWO: No. The additional limitations of an index velocity measuring device and a water stage measuring device for concurrently measuring the index velocity data and the stage data do not integrate the judicial exception into a practical application. The claim does not recite any improvement to the measuring devices, the measurement techniques, or any other technologies. Rather, the additional limitations merely gather data for use by the recited mathematical concept and do not impose a meaningful limit on the judicial exception.
STEP 2B: No. The additional limitations of an index velocity measuring device and a water stage measuring device merely gather index velocity data and stage data that serve as inputs to the mathematical concept recited in claim 12. The measuring devices perform their ordinary functions of obtaining hydrological data, and the remaining system components merely execute the data-driven model using the collected data to generate the forecast. Considered individually and as an ordered combination with the remaining claim elements, these additional limitations do not impose a meaningful limitation on the judicial exception or provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Accordingly, the claim does not recite significantly more than the mathematical concept itself.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 14.
STEP 1. Yes. “The system”. The claim recites:
further comprising a Horizontal Acoustic Doppler Current Profiler configured to provide for measuring index velocity and measuring water stage.
STEP 2A, PRONG ONE: Yes. This claim recites a judicial exception because it depends from claim 12, which recites applying a data-driven model to index velocity data, stage data, historical index velocity data, and historical stage data to forecast at least one of a magnitude of a flood crest arrival and a timing of a flood crest arrival during occurrence of a hydrological event. The recited data-driven model constitutes a mathematical concept and, therefore, an abstract idea. Claim 14 further recites a Horizontal Acoustic Doppler Current Profiler (HADCP) configured to measure index velocity and water stage, which is an additional element.
STEP 2A, PRONG TWO: No. The additional limitation of a Horizontal Acoustic Doppler Current Profiler (HADCP) configured to measure index velocity and water stage does not integrate the judicial exception into a practical application. The claim does not recite any improvement to the operation of the HADCP, the measurement techniques, or any other technology. Rather, the HADCP merely gathers hydrological data that serves as input to the recited mathematical concept and does not impose a meaningful limit on the judicial exception.
STEP 2B: No. The additional limitation of a Horizontal Acoustic Doppler Current Profiler (HADCP) merely gathers index velocity data and water stage data that serve as inputs to the mathematical concept recited in claim 12. The HADCP performs its ordinary function of obtaining hydrological data, and the remaining system components merely execute the data-driven model using the collected data to generate the forecast. Considered individually and as an ordered combination with the remaining claim elements, this additional limitation does not impose a meaningful limitation on the judicial exception or
provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Accordingly, the claim does not recite significantly more than the mathematical concept itself.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 15.
STEP 1: Yes. “The system”. The claim recites:
further comprising a network interface operatively connected to the processor and wherein the set of instructions are further configured to generate an alert in advanced of the flood crest arrival based on at least one of the magnitude of the flood crest arrival and the timing of the flood crest arrival and to communicate the alert through the network interface to a network.
STEP 2A, PRONG ONE: Yes. This claim recites a judicial exception because it depends from claim 14. The recited data-driven model constitutes a mathematical concept and, therefore, an abstract idea. Claim 15 further recites a network interface and generating and communicating an alert through the network interface to a network, which are additional elements.
STEP 2A, PRONG TWO: No. The additional limitations of a network interface operatively connected to the processor and generating and communicating an alert through the network interface to a network do not integrate the judicial exception into a practical application. The claim does not recite any improvement to the operation of the network interface, the processor, network communications, or any other technologies. Rather, the additional limitations merely use the output of the mathematical concept to generate a notification and transmit the notification over a network using conventional computer networking functionality. Accordingly, the additional limitations do not impose a meaningful limit on the judicial exception.
STEP 2B: No. The additional limitations of a network interface operatively connected to the processor and generating and communicating an alert through the network interface to a network merely use the output of the mathematical concept to generate a notification and transmit the notification over a network. The network interface performs its ordinary function of communicating data over the network, and the remaining system components merely execute the data-driven model and provide the resulting forecast for transmission. Considered individually and as an ordered combination with the remaining claim elements, these additional limitations do not impose a meaningful limitation on the judicial exception or provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Accordingly, the claim does not recite significantly more than the mathematical concept itself.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
STEP 16. Yes. The claim recites: “A data-driven method”.
for flood crest characteristics forecasting, the method comprising:
measuring index velocity at an index velocity gaging station of a river or stream to provide index velocity data for flowing water;
simultaneously with measuring the index velocity, measuring water stage at the index velocity gaging station to provide stage data; and
applying a data-driven model implemented using a computing device to the index velocity data and the stage data, the data-driven model further using historical index velocity data and historical stage data collected at the index velocity gaging station to generate a forecast for at least one of magnitude of a flood crest arrival and timing of the flood crest arrival during occurrence of a hydrological event;
wherein the applying the data-driven model uses magnitude of the water stage and index velocity data at onset of an index velocity pulse, rates of change for the index velocity and the water stage associated with the index velocity pulse, duration of rising of the index velocity pulse to a peak, an unsteadiness coefficient, and a time interval between the peak and an associated stage peak.
STEP 2A, PRONG ONE: Claim 16 recites a judicial exception. Specifically, the claim recites applying a data-driven model to measured index velocity data, measured water stage data, historical index velocity data, and historical water stage data to generate a forecast of at least one of the magnitude and timing of a flood crest arrival. The claim further specifies that the data-driven model uses the magnitude of the water stage, index velocity at the onset of an index velocity pulse, rates of change of the index velocity and water stage, duration of the rising index velocity pulse to a peak, an unsteadiness coefficient, and a time interval between the peak and an associated stage peak. These limitations further define the mathematical concept recited by the data-driven model and therefore recite an abstract idea.
STEP 2A, PRONG TWO: No. The additional limitations of measuring index velocity at an index velocity gaging station of a river or stream, simultaneously measuring water stage at the index velocity gaging station, and implementing the data-driven model using a computing device do not integrate the judicial exception into a practical application. The claim does not recite any improvement to the operation of the computing device, the index velocity gaging station, the measuring techniques, or any other technologies. Rather, the additional limitations merely gather data for use by the recited mathematical concept.
STEP 2B: No. The additional limitations of measuring index velocity, measuring water stage, and implementing the data-driven model using a computing device merely gather data that serves as input to the mathematical concept and implement the mathematical concept using a computing device. The measuring steps merely obtain hydrological data, and the computing device merely executes the data-driven model to generate the forecast. Considered individually and as an ordered combination with the remaining claim elements, these additional limitations do not impose a meaningful limitation on the judicial exception or provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Accordingly, the claim does not recite significantly more than the mathematical concept itself.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 17.
STEP 1: Yes. Claim 17 recites: “The data-driven method”.
wherein the index velocity data and the stage data are concurrently collected using a Horizontal Acoustic Doppler Current Profiler (HADCP).
STEP 2A, PRONG ONE: Yes. Claim 17 recites a judicial exception because it depends from claim 16, which recites a mathematical concept. Specifically, claim 16 recites applying a data-driven model to measured and historical data to forecast at least one of the magnitude and timing of a flood crest. Claim 17 further recites concurrently collecting the index velocity data and the stage data using a Horizontal Acoustic Doller Current Profiler (HADCP), which is an additional element.
STEP 2A, PRONG TWO: No. The additional limitation of concurrently collecting the index velocity data and the stage data using a Horizontal Acoustic Doppler Current Profiler (HADCP) does not integrate the judicial exception into a practical application. The claim does not recite any improvement to the operation of the HADCP, the measurement techniques, or any other technologies. Rather, the HADCP is used in its ordinary capacity to obtain data for use by the recited mathematical concept and does not impose a meaningful limit on the judicial exception.
STEP 2B: No. The additional limitation of concurrently collecting the index velocity data and the stage data using a Horizontal acoustic Doppler Current Profiler (HADCP) merely recites the use of a measuring device performing its ordinary function of acquiring measurement data for use by the recited mathematical concept. Considered individually and as an ordered combination with the remaining claim elements, this additional limitation does not amount to significantly more than the judicial exception and does not provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 18.
STEP 1: Yes. The claim recites: “The data-driven method”.
wherein the index velocity data and the stage data are electronically communicated over a network to the computing device.
STEP 2A, PRONG ONE: Yes. This claim recites a judicial exception because it depends from claim 17, which recites applying a data-driven model to measured and historical data to forecast at least one of the magnitude and timing of a flood crest. Claim 18 further recites electronically communicating the index velocity data and the stage data over a network to the computing device, which is an additional element.
STEP 2A, PRONG TWO: No. The additional limitation of electronically communicating the index velocity data and the stage data over a network to the computing device does not integrate the judicial exception into a practical application. The claim does not recite any improvement to network communications, the computing device, or any other technologies. Rather, the additional limitation merely transmits data over a network for use by the recited mathematical concept and does not impose a meaningful limit on the judicial exception.
STEP 2B: No. The additional limitation of electronically communicating the index velocity data and the stage data over a network to the computing device merely transmits data that serves as input to the mathematical concept recited in claim 17. The network merely communicates the measured hydrological data to the computing device, and the computing device merely executes the data-driven model using the communicated data to generate the forecast. Considered individually and as an ordered combination with the remaining claim elements, this additional limitation does not impose a meaningful limitation on the judicial exception or provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Accordingly, the claim does not recite significantly more than the mathematical concept itself.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 19.
STEP 1: Yes. The claim recites: “The data-driven method”.
further comprising generating using the computing device to generate an alert in advance of flood crest based on at least one of the magnitude and the timing of the flood crest arrival.
STEP 2A, PRONG ONE: Yes. The claim recites a judicial exception because it depends from claim 18, which recites applying a data-driven model to measured and historical data to forecast at least one of the magnitude and timing of a flood crest. This claim further recites generating, using the computing device, an alert in advance of the flood crest based on at least one of the magnitude and the timing of the flood crest arrival, which is an additional element.
STEP 2A, PRONG TWO: No. The additional limitation of generating, using the computing device, an alert in advance of the flood crest based on at least one of the magnitude and the timing of the flood crest arrival does not integrate the judicial exception into a practical application. The claim does not recite any improvement to the computing device, the network, or another technology or technical field. Nor does the claim recite using the forecast to control a physical device or otherwise effect a technological improvement. Rather, the additional limitation merely uses the output of the recited mathematical concept to generate a notification and does not impose a meaningful limit on the judicial exception.
STEP 2B: No. The additional limitation of generating, using the computer device, an alert based on the forecast merely recites the output of information using conventional computer functionality. Considered individually and as an ordered combination with the remaining claim elements, this additional limitation does not amount to significantly more than the judicial exception and does not provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 20.
STEP 1: Yes. The claim recites: “The data-driven method”.
wherein the data-driven model provides the forecast for both the magnitude of the flood crest arrival and the timing of the flood crest arrival.
STEP 2A, PRONG ONE: Yes. This claim recites a judicial exception because it depends from claim 19, which recites applying a data-driven model to measured and historical data to forecast flood crest characteristics, thereby reciting a mathematical concept. Claim 20 further specifies that the data-driven model provides the forecast for both the magnitude of the flood crest arrival and the timing of the flood crest arrival. This limitation further defines the recited mathematical concept.
STEP 2A, PRONG TWO: No. Claim 20 does not recite any additional elements beyond those discussed with respect to claim 19 that integrate the judicial exception into a practical application. The additional limitation merely further defines the output of the recited mathematical concept and does not improve the operation of the computing device, the measuring equipment, or any other technologies.
STEP 2B: No. The limitation that the data-driven model provides the forecast for both the magnitude of the flood crest arrival and the timing of the flood crest arrival merely further defines the recited mathematical concept and does not add any additional element that amounts to significantly more than the judicial exception. Considered individually and as an ordered combination with the remaining claim elements, the additional elements do not provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
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, 2, 5 - 8, are rejected under 35 U.S.C. 103 as being unpatentable over Muste_2020 (Augmenting The Operational Capabilities of Sontek/YSI Streamflow Measurement Probes, white paper, April 24, 2020) in view of Baldassarre_2011 (A hydraulic study on the applicability of flood rating curves, Hydrology Research, Vol 42 Issue 1, 10-19, Feb 2011).
Claim 1. Muste_2020 teaches A data-driven method for flood crest characteristics forecasting, the method comprising: measuring index velocity at an index velocity gaging station to provide index velocity data; simultaneously with measuring the index velocity, measuring water stage at the index velocity gaging station to provide stage data (page 2, par 4: “the index-velocity method can directly measure hysteresis in real time by combining the index-velocity (a direct indication of the flow dynamics) and stage (purely geometric descriptor of the flow) measurements.”); and (page 3, par 6: “evaluate how monitoring data collected for the index-velocity method may be used to forecast flood crest magnitude and arrival time without involving any type of modeling.”; page 12, par 1: “the index-velocity method will be subsequently referred to as Index-Velocity Rating Curve (IVRC).”; page 29, table 8: “The phased sequencing of the peaks of the index velocity and stage captured by the IVRC can be used to deliver short-term streamflow forecasts that has the potential to turn many USGS stations into independent forecast points.”).
While Muste_2020 teaches forecasting flood crest magnitude and arrival time using the index-velocity and the stage data, Muste_2020 does not explicitly teach applying a data-driven model implemented using a computing device to the index velocity data and the stage data, applying the data-driven model further using historical index-velocity data and historical stage data collected at the index velocity gaging station.
Baldassarre_2011; however, teaches developing data-driven relationships from previously collected hydraulic data (page 2, par 4: “The standard methodology to derive a rating curve consists of carrying out field campaigns to record contemporaneous measures of water stage h and river discharge Q. Such measures allow us to identify discrete points (Q, h) that are subsequently interpolated through an analytical relationship that approximates the rating curve. The power-law function is commonly used in hydrometric practice…”; par 1: “a number of authors proposed the use of artificial neural networks to model the looped rating curve due to unsteady flow”).
Muste_2020 and Baldassarre_2011 are analogous art because they are from the same field of endeavor called hydrologic forecasting. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to combine Muste_2020 and Baldassarre_2011. The rationale for doing so would have been Muste_2020 teaches forecasting flood crest magnitude and arrival time using the index velocity data and the stage data. Baldassarre_2011 teaches developing data-driven relationships from previously collected hydraulic data. It would have been obvious to combine the index-velocity flood forecasting methodology taught by Muste_2020 with the known data-driven modeling techniques taught by Baldassarre_2011 for the benefit of deriving predictive relationships from the collected hydraulic observations and implementing the forecasting methodology in a data-driven manner. Therefore Muste_2020 in view of Baldassarre_2011 renders obvious “A data-driven method for flood crest characteristics forecasting, the method comprising: measuring index velocity at an index velocity gaging station to provide index velocity data; simultaneously with measuring the index velocity, measuring water stage at the index velocity gaging station to provide stage data; and applying a data-driven model implemented using a computing device to the index velocity data and the stage data, the data-driven model further using historical index velocity data and historical stage data collected at the index velocity gaging station to provide a forecast of at least one of a magnitude of a flood crest arrival during occurrence of a hydrological event and a timing of the flood crest arrival during the occurrence of the hydrological event.”
Claim 2. Muste_2020 in view of Baldassarre_2011 teaches all the limitations of claim 1. Muste_2020 teaches wherein the index velocity data and the stage data are concurrently collected using a Horizontal Acoustic Doppler Current Profiler (HADCP) (page 6, par 3: “The SonTek/YSI HADCP, dubbed by the manufacturer as the Side-Looker (SonTek-SL), is at the core of the index-velocity method by simultaneously and continuously measuring stage and velocities with probes collocated in the same unit”).
Claim 5. Muste_2020 in view of Baldassarre_2011 teaches all the limitations of claim 1. Muste_2020 further teaches wherein the forecast includes both the magnitude of the flood crest arrival and the timing of the flood crest arrival (page 3, par 6: “forecast flood crest magnitude and arrival time”).
Claim 6. Muste_2020 in view of Baldassarre_2011 teaches all the limitations of claim 1. Muste_2020 teaches acquiring the historical index velocity data and the historical stage data to use in the data-driven model (page 2, par 4: “the index-velocity method can directly measure hysteresis in real time by combining the index-velocity (a direct indication of the flow dynamics) and stage (purely geometric descriptor of the flow) measurements.”).
Baldassarre_2011 further teaches constructing predictive relationships from measured hydraulic observations (page 2, par 4: “The standard methodology to derive a rating curve consists of carrying out field campaigns to record contemporaneous measures of water stage h and river discharge Q. Such measures allow us to identify discrete points (Q, h) that are subsequently interpolated through an analytical relationship that approximates the rating curve. The power-law function is commonly used in hydrometric practice…”).
Muste_2022 and Baldassarre_2011 are analogous art because they are from the same field of endeavor called hydrologic forecasting. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to acquire the previously collected index velocity and stage observations taught by Muste_2022 for use in the data-driven predictive methodology suggested by Baldassarre_2011, because using previously acquired hydraulic observations in the forecasting methodology would have been a predictable implementation of the known data-driven modeling technique taught by Baldassarre_2011. Therefore, Muste_2022 in view of Baldassarre_2011 renders obvious further comprising acquiring the historical index velocity data and the historical stage data to use in the data-driven model.
Claim 7. Muste_2020 in view of Baldassarre_2011 teaches all the limitations of claim 1. Muste_2020 further teaches wherein the hydrological event is a single pulse storm (page 21, par 1: “Figure 8a displays the simple case of one precipitation event occurring in the station’s drainage area that in turn generates a simple one-storm hydrograph.”; par 2: “The best scenario for the progression of the present analysis is to first explore the flood wave propagation in its simplest form, i.e., single-storm episode …”; page 26, par 2: “The first analysis step is to trace the variation of the stage following a maximum point (local or absolute) in the index velocity time series. For a single storm these determinations are quite straightforward (see Figure 14a). For multi-storm system it can be observed that each peak is associated with a local maximum in the episode’s time series (see Figure 14b).”).
Claim 8. Muste_2020 in view of Baldassarre_2011 teaches all the limitations of claim 1. Muste_2020 further teaches wherein the hydrological event is a multi-pulse storm (page 21, par 1: “The multiple-storm systems lead to “kinked” hysteretic loops as shown in Figure 8b. It is obvious that the multiple loops associated with a storm episode complicates the interpretation of the hysteretic effect in the IVRC analysis, …”; page 26, par 2: “For multi-storm system it can be observed that each peak is associated with a local maximum in the episode’s time series (see Figure 14b). Samples of visual-based tracing applied to a single- and multi-storm episodes passing through the Henry gaging station are illustrated in Figures 14a and 14b, respectively.”).
Claims 3, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Muste_2020 in view of Baldassarre_2011 in view of Nezu_1997 (Turbulent Structure in Unsteady Depth-Varying Open-Channel Flows, Nezu, I., Kadota, A. & Nakagawa, H., Journal of Hydraulic Engineering 123, 752–763, 1997).
Claim 3. Muste_2020 in view of Baldassarre_2011 teaches all the limitations of claim 2. Muste_2020 teaches wherein the applying the data-driven model uses magnitude of the stage data and the index velocity data (page 2, par 4: “the index-velocity method can directly measure hysteresis in real time by combining the index-velocity … and stage … measurements.”) at onset of an index velocity pulse, rates of change for the index velocity (page 27, par 2: “we realized that the hysteresis magnitude is not sufficiently characterized by only retaining the state of the variable at their peaks, but also on the ‘memory’ of the flow to get to that state, as actually implied by the shear definition of hysteresis (Prowse, 1984). Consequently, we clustered the available dataset in groups using an additional parameter that takes into account not only the state of the index-velocity but also the gradient associated with its rising. This parameter, aimed to define the intensity of the storm, is labeled herein as ‘Flood Wave Intensity Gradient’ (FWIG), defined as G = ΔVindex/ΔT, with ΔT with ΔT measured on the flood wave time series scale (hours). The parameter is associated only with the rising portion of the index-velocity hydrograph”) and [rates of change for the] stage (page 22, par 1: “A quantitative measure for the storm intensity can be considered the ratio between magnitude of the stage peak and the storm “base time” … i.e. ΔH/ΔT, with ΔT measured on the flood wave time series scale (hours). … The ratio, dimensionally defining a velocity, is related with the speed of stage variation during the flow wave propagation …”) associated with the index velocity pulse, duration of rising of the index velocity pulse (Note: ΔT was mentioned above.) to an index velocity peak, (page 26, par 2: “During the tracing we also retained two associated parameters: a) the time interval between the index-velocity peak and the associated peak in the stage (or inflexion point for some of the multiple-storms), and, b) the magnitude of both variables at the identified peaks.”).
Muste_2020 in view of Baldassarre_2011 does not explicitly teach an unsteadiness coefficient.
Nezu_1997; however, teaches “unsteadiness coefficient” (page 2, par 5: “Therefore, Nezu and Nakagawa (1993b) have proposed the following parameter a that correlates with the pressure gradient
dP/dx:
α
=
1
U
c
∂
h
∂
t
=
1
U
c
h
p
-
h
b
T
d
=
V
s
U
c
where Uc = convection velocity of turbulent eddies and is roughly equal to (Ub + Up)/2. The unsteadiness parameter [coefficient] α …”).
Muste_2020 and Baldassarre_2011 and Nezu_1997 are analogous art because they are from the same field of endeavor called hydrologic forecasting. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to combine Muste_2020 and Baldassarre_2011 and Nezu_1997. The rationale for doing so would have been that Muste_2020 in view of Baldassarre_2011 teaches measuring and analyzing the magnitude, duration, rate of change, hysteresis, and peak phasing of the index velocity and the stage during flood-wave propagation. Nezu_1997 teaches an unsteadiness coefficient α that quantitatively characterizes unsteady-flow behavior based on the rate of change of water depth relative to a representative flow velocity. Therefore it would have been obvious to combine the data taught by Muste_2020 in view of Baldassarre_2011 with the unsteadiness coefficient α taught by Nezu_1997 for the benefit of further characterizing the unsteadiness of the flood wave and thereby provide additional information for the flood-forecasting model.
Claim 16. The limitations of claim 16 are substantially the same as those of claim 1 and the additional limitations of claim 3 concerning the parameters used in applying the data-driven model and are rejected due to the same reasons as outlined above for claim 1 and claim 3.
Claim 17. Muste_2020 in view of Baldassarre_2011 in view of Nezu_1997 teaches all the limitations of claim 16. The additional claim are substantially the same as those of claim 2, and are rejected due to the same reasons as outlined above for claim 2.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Muste_2020 in view of Baldassarre_2011 in view of Mileti_1990 (Communication of Emergency Public Warnings – A Social Science Perspective and State-of-the-Art Assessment, Dennis S. Mileti, John H. Sorensen, Oak Ridge National Laboratory, Oak Ridge, Tennessee, August, 1990) in view of Piazzi_2021 (Sequential Data Assimilation for Streamflow Forecasting: Assessing the Sensitivity to Uncertainties and Updated Variables of a Conceptual Hydrological Model at Basin Scale, G. Piazzi, G. Thirel, C. Perrin, O. Delaigue, Water Resources Research, Volume 57, Issue 4, April 20, 2021).
Claim 4. Muste_2020 in view of Baldassarre_2011 teaches all the limitations of claim 1. Muste_2020 teaches further comprising generating (page 3, par 6: “evaluate how monitoring data collected for the index-velocity method may be used to forecast flood crest magnitude and arrival time without involving any type of modeling.”; page 12, par 1: “the index-velocity method will be subsequently referred to as Index-Velocity Rating Curve (IVRC).”; page 29, table 8: “The phased sequencing of the peaks of the index velocity and stage captured by the IVRC can be used to deliver short-term streamflow forecasts that has the potential to turn many USGS stations into independent forecast points.”).
Muste_2020 in view of Baldassarre_2011 does not explicitly teach “using the computing device to generate an alert in advance of flood crest based on the forecast”.
Mileti_1990; however, teaches (page 27, par 2: “Flood warning systems follow four steps: collection of data, transmittal of data, analysis of the data and flood forecasting, and alerting of officials. … The forecast, which generally includes timing and magnitude of the flood, is given to officials responsible for flood warning.”; par 3: “An automated system may use a series of automated rain and stream gages to radio-transmit data to a central computer facility. These data are fed into a hydrological model. When a critical parameter is met, a beeper is activated to alert a local official. Some systems combine both manual and automated techniques …”).
Muste_2022 and Baldassarre_2011 and Mileti_1990 are analogous art because they are from the same field of endeavor called hydrologic forecasting. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to combine Muste_2022 and Baldassarre_2011 with Mileti_1990. The rationale for doing so would have been Muste_2022 in view of Baldassarre_2011 teaches forecasting the magnitude and the timing of the flood crest arrival. Mileti_1990 teaches generating an alert based on the flood forecast. It would have been obvious to combine the flood forecasting taught by Muste_2022 in view of Baldassarre_2011 with the generation of an alert based on the flood forecast taught by Mileti_1990 for the benefit of enabling personnel to receive timely notice of an impending flood crest and take appropriate action before the forecasted event occurred. Therefore, Muste_2020 in view of Baldassarre_2011 in view of Mileti_1990 teaches further comprising generating
Muste_2020 in view of Baldassarre_2011 in view of Mileti_1990 does not teach
Piazzi_2021; however, teaches computer-implemented hydrological streamflow forecasting using a computational algorithm and a hydrologic model (page 4, par 6: “Filtering techniques make it possible to readily process the observational data (Yt) as they become available and to sequentially update the model state at time t (Xt) …” and that the model state is defined using “… the dynamic model operator, which calls for the model input vector (Ut), the vector of the model parameters (θ), and the unknown model error (Ωt)”; page 5, par 2: “Both EnKF and PF rely on a recursive Bayesian algorithm … The main difference between the EnKF and the PF is how they recursively generate an approximation to the probability distributions of the prognostic variables by using a set of randomly generated model replicates according to a Monte Carlo approach”), and the implementation of the forecasting methodology was a software package (page 25, par 1: “An R package named airGRdatassim … is available on GitLab … airGRdatassim is a package based on the air-GR hydrological modeling package and it provides the tools to perform the assimilation of the observed discharges via Ensemble Kalman filter or Particle filter, according to the methodology presented in this paper.”.).
Muste_2020 and Baldassarre_2011 and Mileti_1990 and Piazzi_2021 are analogous art because they are from the same field of endeavor called hydrologic forecasting. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to combine Muste_2020 and Baldassarre_2011 and Mileti_1990 and Piazzi_2021. The rationale for doing so would have been Muste_2020 in view of Baldassarre_2011 in view of Mileti_1990 teaches generating an alert based on the flood forecast. Piazzi_2021 teaches computer-implemented hydrologic forecasting using a computational algorithm implemented in a software package. Therefore, it would have been obvious to combine flood forecasting and alert generation taught by Muste_2020 in view of Baldassarre_2011 in view of Mileti_1990 using the computer-implemented forecasting techniques taught by Piazzi_2021 for the benefit of improving the speed, automation, and reliability of the flood warning process system. Therefore Muste_2020 in view of Baldassarre_2011 in view of Mileti_1990 in view of Piazzi_2021 renders obvious further comprising generating using the computing device to generate an alert in advance of flood crest based on the forecast of at least one of the magnitude of the flood crest arrival and the timing of the flood crest arrival.
Claims 9 - 14 are rejected under 35 U.S.C. 103 as being unpatentable over Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021.
Claim 9. Muste_2020 in view of Baldassarre_2011 teaches all the limitations of claim 1. Muste_2020 in view of Baldassarre_2011 does not explicitly teach further comprising calibrating a predictive channel routing numerical model using the forecast.
Piazzi_2021; however, teaches calibrating a predictive numerical hydrological model having a channel-routing component (page 7, par 1: “The routing function relies on a nonlinear routing store (R) and a symmetric unit hydrograph for runoff lagging. The unit hydrograph states (UH) define the streamflow that is routed at each time step.”); calibrating parameters of the predictive model, including a parameter of the routing store (page 7, par 2: “The rainfall–runoff model relies on five free parameters … requiring proper calibration to optimize the accuracy of model simulations: the maximum capacities of both the production and routing stores (X1 [mm] and X3 [mm], respectively);”; par 3: “For each watershed, GR5J was calibrated throughout the 6-year analysis period using the Kling–Gupta efficiency (KGE) coefficient … as an objective function,”); using simulated and observed values in an updating process (page 5, par 2: “Whenever an observation is available, an analysis procedure is performed through optimal weighting between simulated and observed values, with the degree of correction determined by their degree of uncertainty.”); and updating model parameters as part of the process (page 5, par 5: “The EnKF technique also allows for combined state–parameter estimation. One approach is the so-called state augmentation, where parameters are treated as model states and are concatenated with them into a single joint vector (including both θ and Xt) updated by the DA analysis procedure when observations are available (Reichle et al., 2002).”).
Muste_2020 and Baldassarre_2011 and Piazzi_2021 are analogous art because they are from the same field of endeavor called hydrologic forecasting. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to combine Muste_2022 and Baldassarre_2011 and Piazzi_2021. The rationale for doing so would have been Muste_2020 in view of Baldassarre_2011 teaches generating a forecast of flood-crest magnitude and arrival time using hydrologic data. Piazzi_2021 teaches a predictive hydrological model having a routing component and updating model parameters as part of a simulation-and-updating process. It would have been obvious to use the forecast taught by Muste_2020 in view of Baldassarre_2011 in the model-updating and parameter-calibration process taught by Piazzi_2021 for the benefit of updating the predictive routing model based on current hydrologic information represented by the forecast and improving the model’s representation of the predicted flood event and the accuracy of subsequent predictions. Therefore, Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021 renders obvious calibrating a predictive channel routing numerical model using the forecast.
Claim 10. Muste_2020 in view of Baldassarre_2011 teaches all the limitations of claim 1. Muste_2020 in view of Baldassarre_2011 does not explicitly teach further comprising validating a predictive channel routing numerical model using the forecast.
Piazzi_2021; however, teaches validating a predictive numerical hydrological model having a channel-routing component (page 7, par 1: “The routing function relies on a nonlinear routing store (R) and a symmetric unit hydrograph for runoff lagging. The unit hydrograph states (UH) define the streamflow that is routed at each time step.”); and evaluating the accuracy and performance of the predictive model using forecast verification metrics (page 14, par 7: “To avoid possible misevaluation, over and under evaluation, both deterministic and probabilistic verification metrics are evaluated for each lead time to properly analyze the different attributes of streamflow forecasts … The root mean square error (RMSE) is evaluated for the single-valued ensemble mean forecast, with the aim of evaluating model accuracy (lower values indicate better forecasts). The overall accuracy of DA-based forecasts is assessed against the accuracy of OL predictions by evaluating the continuous ranked probability skill score (CRPSS)”).
Muste_2020 and Baldassarre_2011 and Piazzi_2021 are analogous art because they are from the same field of endeavor called hydrologic forecasting. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to combine Muste_2022 and Baldassarre_2011 and Piazzi_2021. The rationale for doing so would have been Muste_2020 in view of Baldassarre_2011 teaches generating a forecast of flood-crest magnitude and arrival time using hydrologic data. Piazzi_2021 teaches a predictive hydrologic model having a routing component and evaluating model accuracy using verification metrics applied to streamflow forecasts. It would have been obvious to use the forecast taught by Muste_2020 in view of Baldassarre_2011 to validate the predictive routing model taught by Piazzi_2021 for the benefit of evaluating the accuracy and performance of the predictive routing model and determining whether the model adequately represents the predicted flood event. Therefore, Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021 renders obvious further comprising validating a predictive channel routing numerical model using the forecast.
Claim 11. Muste_2020 in view of Baldassarre_2011 teaches all the limitations of claim 1. Baldassarre_2011 further teaches wherein the data-driven model is implemented using (page 2, par 4: “The standard methodology to derive a rating curve consists of carrying out field campaigns to record contemporaneous measures of water stage h and river discharge Q. Such measures allow us to identify discrete points (Q, h) that are subsequently interpolated through an analytical relationship that approximates the rating curve. The power-law function is commonly used in hydrometric practice …”).
Muste_2020 in view of Baldassarre_2011 does not explicitly teach that in a machine-readable non-transitory medium of the computing device and executed by at least one processor of the computing device.
Piazzi_2021; however, teaches computer-implemented hydrological streamflow forecasting using a computational algorithm and a hydrologic model (page 4, par 6: “Filtering techniques make it possible to readily process the observational data (Yt) as they become available and to sequentially update the model state at time t (Xt) …” and that the model state is defined using “… the dynamic model operator, which calls for the model input vector (Ut), the vector of the model parameters (θ), and the unknown model error (Ωt)”; page 5, par 2: “Both EnKF and PF rely on a recursive Bayesian algorithm … The main difference between the EnKF and the PF is how they recursively generate an approximation to the probability distributions of the prognostic variables by using a set of randomly generated model replicates according to a Monte Carlo approach”), and the implementation of the forecasting methodology was a software package (page 25, par 1: “An R package named airGRdatassim … is available on GitLab … airGRdatassim is a package based on the air-GR hydrological modeling package and it provides the tools to perform the assimilation of the observed discharges via Ensemble Kalman filter or Particle filter, according to the methodology presented in this paper.”).
Muste_2020 and Baldassarre_2011 and Piazzi_2021 are analogous art because they are from the same field of endeavor called hydrologic forecasting. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to combine Muste_2020 and Baldassarre_2011 and Piazzi_2021. The rationale for doing so would have been Muste_2020 in view of Baldassarre_2011 teaches implement the data-driven model and its regression relationships using historical data. Piazzi_2021 teaches computer-implemented hydrologic forecasting using a computational algorithm implemented in a software package. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to implement the data-driven model and its regression relationships taught by Muste_2020 in view of Baldassarre_2011 as computer-executable instructions and stored data on a non-transitory machine-readable medium for execution by a processor taught by Piazzi_2021 for the benefit of improving accuracy of the data modeling for a more accurate and faster flood forecasting. Therefore, Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021 renders obvious wherein the data-driven model is implemented using a set of instructions and regression lines determined from historical data stored in a machine-readable non-transitory medium of the computing device and executed by at least one processor of the computing device.
Claim 12. Muste_2020 teaches A system for implementing a data-driven method for streamflow forecasting,
(page 3, par 6: “evaluate how monitoring data collected for the index-velocity method may be used to forecast flood crest magnitude and arrival time without involving any type of modeling.”; page 29, table 8: “The phased sequencing of the peaks of the index velocity and stage captured by the IVRC can be used to deliver short-term streamflow forecasts that has the potential to turn many USGS stations into independent forecast points.”).
Muste_2020 does not explicitly teach that
a memory;
a processor operatively connected to the memory; and
a set of instructions stored on the memory for execution by the processor
Baldassarre_2011; however, teaches developing data-driven relationships from previously collected hydraulic data (page 2, par 4: “The standard methodology to derive a rating curve consists of carrying out field campaigns to record contemporaneous measures of water stage h and river discharge Q. Such measures allow us to identify discrete points (Q, h) that are subsequently interpolated through an analytical relationship that approximates the rating curve. The power-law function is commonly used in hydrometric practice…”; par 1: “a number of authors proposed the use of artificial neural networks to model the looped rating curve due to unsteady flow”).
Muste_2020 and Baldassarre_2011 are analogous art because they are from the same field of endeavor called hydrologic forecasting. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to combine Muste_2020 and Baldassarre_2011. The rationale for doing so would have been Muste_2020 teaches forecasting flood crest magnitude and arrival time using the index velocity data and the stage data. Baldassarre_2011 teaches developing data-driven relationships from previously collected hydraulic data. It would have been obvious to combine the known data-driven modeling techniques taught by Baldassarre_2011 with the index-velocity flood forecasting methodology taught by Muste_2020 for the benefit of deriving predictive relationships from the collected hydraulic observations and implementing the forecasting methodology in a data-driven manner. Therefore, Muste_2020 in view of Baldassarre_2011 renders obvious A system for implementing a data-driven method for streamflow forecasting,
Muste_2020 in view of Baldassarre_2011 does not teach
a memory;
a processor operatively connected to the memory; and
a set of instructions stored on the memory for execution by the processor wherein the set of instructions are
Piazzi_2021; however, teaches computer-implemented hydrological streamflow forecasting using a computational algorithm and a hydrologic model (page 4, par 6: “Filtering techniques make it possible to readily process the observational data (Yt) as they become available and to sequentially update the model state at time t (Xt) …” and that the model state is defined using “… the dynamic model operator, which calls for the model input vector (Ut), the vector of the model parameters (θ), and the unknown model error (Ωt)”; page 5, par 2: “Both EnKF and PF rely on a recursive Bayesian algorithm … The main difference between the EnKF and the PF is how they recursively generate an approximation to the probability distributions of the prognostic variables by using a set of randomly generated model replicates according to a Monte Carlo approach”), and the implementation of the forecasting methodology was a software package (page 25, par 1: “An R package named airGRdatassim … is available on GitLab … airGRdatassim is a package based on the air-GR hydrological modeling package and it provides the tools to perform the assimilation of the observed discharges via Ensemble Kalman filter or Particle filter, according to the methodology presented in this paper.”).
Muste_2020 and Baldassarre_2011 and Piazzi_2021 are analogous art because they are from the same field of endeavor called hydrologic forecasting. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to combine Muste_2020 and Baldassarre_2011 and Piazzi_2021. The rationale for doing so would have been Muste_2020 in view of Baldassarre_2011 teaches implementing the data-driven streamflow forecasting methodology. Piazzi_2021 teaches computer-implemented hydrologic forecasting using a computational algorithm implemented in a software package. It would have been obvious to implement the computer-implemented hydrologic forecasting technique taught by Piazzi_2021 on a conventional computing system comprising a processor operatively connected to a memory and instructions stored in the memory for execution by the processor in order to execute the forecasting software and processing the hydrologic data. It would have been obvious to combine the data-driven streamflow forecast taught by Muste_2020 in view of Baldassarre_2011 with the computer-implemented hydrologic forecasting technique taught by Pazzi_2021 for the benefit of improving accuracy of the data modeling for a more accurate and faster flood forecasting. Therefore, Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021 renders obvious A system for implementing a data-driven method for streamflow forecasting, the system comprising:
a memory;
a processor operatively connected to the memory; and
a set of instructions stored on the memory for execution by the processor wherein the set of instructions are configured to apply a data-driven model to index velocity data and stage data, the data-driven model further using historical index velocity data and historical stage data to forecast at least one of a magnitude of a flood crest arrival and a timing of a flood crest arrival during occurrence of a hydrological event.
Claim 13. Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021 teaches all the limitations of claim 12. Muste_2020 further teaches further comprising an index velocity measuring device for measuring the index velocity data and a water stage measuring device for measuring the stage data concurrently with the measuring of the index velocity data (page 2, par 4: “the index-velocity method can directly measure hysteresis in real time by combining the index-velocity (a direct indication of the flow dynamics) and stage (purely geometric descriptor of the flow) measurements.”).
Claim 14. Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021 teaches all the limitations of claim 12. Muste_2020 further teaches further comprising a Horizontal Acoustic Doppler Current Profiler configured to provide for measuring index velocity and measuring water stage (page 6, par 2: “Many of the nationwide index-velocity stations are equipped with SonTek/YSI Horizontal Acoustic Doppler Current Profilers (HADCP). The SonTek/YSI HADCP, dubbed by the manufacturer as the Side-Looker (SonTek-SL), is at the core of the index-velocity method by simultaneously and continuously measuring stage and velocities with probes collocated in the same unit …”; page 13, par 1: “The datasets for index-velocity entail direct streamflow measurements paired with real-time, continuous measurements with HADCPs and stage measurements sensors (typically pressure transducers). SonTek-SL can acquire the two variables within the same instrument.”).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021 in view of Mileti_1990.
Claim 15. Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021 teaches all the limitations of claim 14. Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021 does not explicitly teach further comprising a network interface operatively connected to the processor and wherein the set of instructions are further configured to generate an alert in advanced of the flood crest arrival based on at least one of the magnitude of the flood crest arrival and the timing of the flood crest arrival and to communicate the alert through the network interface to a network.
Mileti_1990; however, teaches (page 27, par 2: “Flood warning systems follow four steps: collection of data, transmittal of data, analysis of the data and flood forecasting, and alerting of officials. ... The forecast, which generally includes timing and magnitude of the flood, is given to officials responsible for flood warning.”; par 3: “An automated system may use a series of automated rain and stream gages to radio-transmit data to a central computer facility. These data are fed into a hydrological model. When a critical parameter is met, a beeper is activated to alert a local official. Some systems combine both manual and automated techniques …”).
Muste_2020 and Baldassarre_2011 and Piazzi_2021 and Mileti_1990 are analogous art because they are from the same field of endeavor called hydrologic forecasting. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to combine Muste_2020 and Baldassarre_2011 and Piazzi_2021 with Mileti_1990. The rationale for doing so would have been that Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021 teaches a computer-implemented system for forecasting flood-crest magnitude and arrival time using hydrologic data. Mileti_1990 teaches generating an alert based on a flood forecast that includes the timing and magnitude of the flood and communicating flood-warning information to responsible officials. It would have been obvious to further configure the set of instructions of the computer-implemented forecasting system taught by Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021 to generate an alert, as taught by Mileti_1990, based on the forecasted flood-crest magnitude or arrival time for the benefit of providing timely flood-warning information to responsible personnel before the forecasted flood crest arrives. It would further have been obvious to provide the computer-implemented forecasting system with a network interface operatively connected to the processor and to further configure the set of instructions to communicate the alert through the network interface to a network, because such an arrangement would have been a predictable implementation for enabling the computer-implemented forecasting system to electronically communicate the generated alert over a network. Therefore, Muste_2020 in view of Baldassarre_2011 in view of Piazzi_2021 in further view of Mileti_1990 renders obvious further comprising a network interface operatively connected to the processor and wherein the set of instructions are further configured to generate an alert in advanced of the flood crest arrival based on at least one of the magnitude of the flood crest arrival and the timing of the flood crest arrival and to communicate the alert through the network interface to a network.
Claims 18, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Muste_2020 in view of Baldassarre_2011 in view of Nezu_1997 in view of Mileti_1990.
Claim 18. Muste_2020 in view of Baldassarre_2011 in view of Nezu_1997 teaches all the limitations of claim 17. Muste_2020 in view of Baldassarre_2011 in view of Nezu_1997 does not explicitly teach wherein the index velocity data and the stage data are electronically communicated over a network to the computing device.
Mileti_1990; however, teaches wherein the index velocity data and the stage data are electronically communicated over a network to the computing device (page 27, par 3: “An automated system may use a series of automated rain and stream gages to radio-transmit data to a central computer facility.”).
Muste_2020 and Baldassarre_2011 and Nezu_1997 and Mileti_1990 are analogous art because they are from the same field of endeavor called hydrologic forecasting. Before the effective filing date it would have been obvious to a person of ordinary skill in the art to combine Muste_2020 and Baldassarre_2011 and Nezu_1997 and Mileti_1990. The rationale for doing so would have been that Muste_2020 in view of Baldassarre_2011 in view of Nezu_1997 teaches stream flow data collection. Mileti_1990 teaches using automated stream gages to transmit stream flow data to the computing device. Therefore it would have been obvious to combine the stream data collection taught by Muste_2020 in view of Baldassarre_2011 in view of Nezu_1997 with the stream data transmission taught by Mileti_1990 for the benefit of enabling remotely collected data to be provided to the computing device for application of the data-driven forecasting model. Muste_2020 in view of Baldassarre_2011 in view of Nezu_1997 in view of Mileti_1990 renders obvious wherein the index velocity data and the stage data are electronically communicated over a network to the computing device.
Claim 19. Muste_2020 in view of Baldassarre_2011 in view of Nezu_1997 in view of Mileti_1990 teaches all the limitations of claim 18. Mileti_1990 further teaches further comprising generating using the computing device to generate an alert in advance of flood crest based on at least one of the magnitude and the timing of the flood crest arrival (page 27, par 2: “Flood warning systems follow four steps: collection of data, transmittal of data, analysis of the data and flood forecasting, and alerting of officials. ... The forecast, which generally includes timing and magnitude of the flood, is given to officials responsible for flood warning.”; par 3: “An automated system may use a series of automated rain and stream gages to radio-transmit data to a central computer facility. These data are fed into a hydrological model. When a critical parameter is met, a beeper is activated to alert a local official. Some systems combine both manual and automated techniques …”).
Claim 20. Muste_2020 in view of Baldassarre_2011 in view of Nezu_1997 in view of Mileti_1990 teaches all the limitations of claim 19. Muste_2020 further teaches wherein the data-driven model provides the forecast for both the magnitude of the flood crest arrival and the timing of the flood crest arrival (page 27, par 2: “The blue line in this figure represents the functional relationships for the index-velocity peak at a given time and the lag for the associated stage crest. The green line represents the functional relationship between the index-velocity peak and the magnitude of the stage crest.”).
Conclusion
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/Q.L./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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