DETAILED ACTION
Claims 1-15 are currently presented for examination.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted has been considered by the Examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 1200F. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 3 is objected to because of the following informalities: it recites blended data when it is not the first recitation. Appropriate correction is required.
Claim 5 is objected to because of the following informalities: the claim recites a power spectral density when it is not the first recitation. 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.
Regarding claims 1-15, are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-13 are directed to a method, which is a process, which is a statutory category of invention. Claim 14 is directed to a system, which is a machine, which is a statutory category of invention. Claim 15 is directed to a non-transitory computer readable medium, which is a manufacture, which is a statutory category of invention. Therefore, claims 1-15 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 14 and 15 recite the abstract idea of blending simulated and raw data, constituting an abstract idea based on Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper. The limitation of "obtaining simulated event data comprising a simulated event and authentic raw data; and” covers mental processes including observing a dataset with two different types of data. Additionally, the limitation of “combining the simulated event data and the authentic raw data to form blended data that comprises the simulated event.” covers mental processes including making a judgement on how to combine the data. Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper.
Dependent claims 2-13 further narrow the abstract ideas, identified in the independent claims.
Step 2A, Prong 2: The judicial exception is not integrated into a practical application. In Claim 7, the additional element of “a generative adversarial network”, as well as “a generator”, and “a discriminator” in claim 8, as well as “a processor”, “a database” and “a memory” in claim 14, as well as “a non-transitory computer readable medium” and “a processor” in claim 15 merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation of “wherein the authentic raw data is obtained and combined with the simulated event data in real-time” in claim 12, are mere instructions to implement an abstract idea using a computer in its ordinary capacity or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(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 mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) The additional limitation of “wherein obtaining the authentic raw data comprises performing optical fiber interferometry using fiber Bragg gratings” in claim 10, “wherein obtaining the authentic raw data comprises performing distributed acoustic sensing alternatively” in claim 11, and “wherein the authentic raw data is obtained by recording acoustics proximate a pipeline, and wherein the simulated event comprises a pipeline leak” in claim 13 can be viewed as is insignificant extra-solution activity, specifically pertaining to mere data gathering necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to testing a system for a response, the response being used to determine system malfunction, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-13 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above.
Step 2B: Claims 1, 14 and 15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In Claim 7, the additional element of “a generative adversarial network”, as well as “a generator”, and “a discriminator” in claim 8, as well as “a processor”, “a database” and “a memory” in claim 14, as well as “a non-transitory computer readable medium” and “a processor” in claim 15 merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation of “wherein the authentic raw data is obtained and combined with the simulated event data in real-time” in claim 12, are mere instructions to implement an abstract idea using a computer in its ordinary capacity or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(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 mental process) does not amount to significantly more. (MPEP 2106.05(f)(2)) The additional limitation of “wherein obtaining the authentic raw data comprises performing optical fiber interferometry using fiber Bragg gratings” in claim 10, “wherein obtaining the authentic raw data comprises performing distributed acoustic sensing alternatively” in claim 11, and “wherein the authentic raw data is obtained by recording acoustics proximate a pipeline, and wherein the simulated event comprises a pipeline leak” in claim 13 can be viewed as is insignificant extra-solution activity, specifically pertaining to mere data gathering necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to amount to significantly more. This is akin to testing a system for a response, the response being used to determine system malfunction, which has been identified as extra solution activity. Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claim 2 is directed to further defining how the data is processed, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 3 is directed to further defining how the data is combined, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 4 is directed to further defining the type of blended data, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 5 is directed to further defining how the data is expressed, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 6 is directed to further defining what the simulated data is, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 7 is directed to further defining what the simulated data is, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 9 is directed to further defining the authentic raw data is, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Accordingly, claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 12 and 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Koivisto et al. “Combination of meteorological reanalysis data and stochastic simulation for modelling wind generation variability.”
Regarding claim 1, Koivisto anticipates (a) obtaining simulated event data comprising a simulated event and authentic raw data; and (Abstract, Sections 1 and 2, the model creates simulated wind generation events, two years of authentic measurements are used for comparison)
(b) combining the simulated event data and the authentic raw data to form blended data that comprises the simulated event. (Abstract, Sections 1, 2, 2.3, 6, simulated and measured data combined for modeling variability)
Regarding claim 2, Koivisto anticipates the limitations of claim 1. Koivisto also anticipates further comprising subsequently processing the blended data and identifying the simulated event therein. (Abstract, Sections 1 and 2, simulation and measured data are combined to form simulation data)
Regarding claim 3, Koivisto anticipates the limitations of claim 1. Koivisto also anticipates wherein combining the simulated event data and the raw data to form blended data comprises: (a) respectively converting the simulated event data and the authentic raw data into frequency domain representations thereof; (Sections 1 and 2, corres combines the simulation and measured data in WRF format; Sec 2.2-2.3, 4.2-4.3, Figures 2, 7, 9 and 11, frequency domain representations are made)
(b) summing the frequency domain representations of the simulated event data and the authentic raw data together to form a frequency domain representation of the blended data; and (Sections 1, 2-2.3, Figures 2, 7, 9 and 11, corres sums the simulated and measured data when making a combination then forms a frequency domain representation shown in the figures)
(c) converting the frequency domain representation of the blended data into a time domain representation of the blended data. (Sections 3.2-3.3 and 4.3, Figures 4, 8, 10-12, a time domain representation is created)
Regarding claim 4, Koivisto anticipates the limitations of claim 1. Koivisto also anticipates wherein the blended data is expressed as a power spectral density. (Figures 2, 7, 9 and 11, Section 2.2, 4.2-4.3, 5, combined data is expressed as a power spectral density)
Regarding claim 5, Koivisto anticipates the limitations of claim 4. Koivisto also anticipates wherein the simulated event data is expressed as a power spectral density when combined with the authentic raw data. (Figures 2, 7, 9 and 11, Section 1, 2-2.3, 4.2-4.3, 5, the simulated data is expressed as a PSD even when combined)
Regarding claim 6, Koivisto anticipates the limitations of claim 1. Koivisto also anticipates wherein the simulated event data comprises recorded authentic events. (Abstract, Sections 1-2.3, the simulated data resulting from the combined simulated and measured data is formed with and therefore comprises real measured data)
Regarding claim 12, Koivisto anticipates the limitations of claim 1. Koivisto also anticipates wherein the authentic raw data is obtained and combined with the simulated event data in real-time. (Figures 2, 3-5, 7, 9 and 11, Sections 3-3.3, the data is combined in real-time with Matlab and gathered in real time with 0 lag and from time T=0 forward)
In regards to claim 14, it is the system embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1. The additional computer components are taught by Koivisto using Matlab. (Figures 2, 3-5, 7, 9 and 11)
In regards to claim 15, it is the computer readable medium embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Koivisto in view of Azevedo et al. “Generative adversarial network as a stochastic subsurface model reconstruction.”
Regarding claim 7, Koivisto anticipates the limitations of claim 5. Koivisto teaches further comprising generating the simulated event data …, wherein some of the authentic raw data is input … to permit generation of the simulated event data. (Abstract, Sections 1 and 2, simulation and measured data are combined to form simulation data)
Koivisto does not explicitly teach using a generative adversarial network
Azevedo teaches using a generative adversarial network (Sections 2.1, 3.1-4.1, a GAN is used to return simulated data)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Koivisto with Azevedo as the references deal with simulation, in order to implement a system that uses a GAN in the simulation process with a discriminator and a generator. Azevedo would modify Koivisto by using a GAN in the simulation process with a discriminator and a generator. The benefit of doing so is the GAN can learn from sets of simulations, reproduce complex data, and use observed datasets as represented by direct measurements as conditioning data for model generation. (Azevedo Section 1)
Regarding claim 8, the combination of Koivisto and Azevedo teaches the limitations of claim 6. Koivisto does not explicitly teach wherein the generative adversarial network comprises a generator and a discriminator, wherein all layers except an output layer of the discriminator use leaky rectified linear unit activation, the output layer of the discriminator uses tanh activation, and all layers of the generator use leaky rectified linear unit activation.
Azevedo teaches wherein the generative adversarial network comprises a generator and a discriminator, wherein all layers except an output layer of the discriminator use leaky rectified linear unit activation, the output layer of the discriminator uses tanh activation, and all layers of the generator use leaky rectified linear unit activation. (Sections 2.1-3.1, Figures 1 and 2, a GAN is used with a generator and a discriminator, that use a TANH and leaky rectified linear unit activation)
See motivation of claim 7.
Claim 9 and 13 is rejected under 35 U.S.C. 103 as being unpatentable over Koivisto in view of Wong et al. “Failure Detection Methods for Pipeline Networks: From Acoustic Sensing to Cyber-Physical Systems.”
Regarding claim 9, Koivisto anticipates the limitations of claim 1. Koivisto does not explicitly recite wherein the authentic raw data comprises acoustic data.
Wong teaches wherein the authentic raw data comprises acoustic data. (Section 1, Table 1, acoustic sensor are used to detect leaks)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Koivisto with Wong as the references deal with simulation, in order to implement a system that senses acoustic data. Wong would modify Koivisto by using an acoustic sensor to gather acoustic data. The benefit of doing so is with relatively simple computations, the acoustic approach provides an accurate indication of the location of leakages. (Wong Section 2.6)
Regarding claim 13, Koivisto anticipates the limitations of claim 1. Koivisto does not explicitly recite wherein the authentic raw data is obtained by recording acoustics proximate a pipeline, and wherein the simulated event comprises a pipeline leak.
Wong teaches wherein the authentic raw data is obtained by recording acoustics proximate a pipeline, and wherein the simulated event comprises a pipeline leak. (Abstract, Section 1, Table 1, acoustic sensor are used to detect leaks)
See motivation of claim 9
Claims 10-11 is rejected under 35 U.S.C. 103 as being unpatentable over Koivisto in view of Azevedo and in further view of Wong.
Regarding claim 10, the combination of Koivisto and Azevedo teaches the limitations of claim 8. The combination of Koivisto and Azevedo does not explicitly teach wherein obtaining the authentic raw data comprises performing optical fiber interferometry using fiber Bragg gratings.
Wong teaches wherein obtaining the authentic raw data comprises performing optical fiber interferometry using fiber Bragg gratings. (Section 2.12, Fibre Bragg Grating (FBG) is a type of fibre optic sensor used)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Koivisto and Azevedo with Wong as the references deal with simulation, in order to implement a system that uses fiber Bragg gratings. Wong would modify Koivisto and Azevedo by using fiber Bragg gratings. The benefit of doing so is the core of the fibre can be modulated according to the type of sensing required. (Wong Section 2.12)
Regarding claim 11, the combination of Koivisto and Azevedo teaches the limitations of claim 8. The combination of Koivisto and Azevedo does not explicitly teach wherein obtaining the authentic raw data comprises performing distributed acoustic sensing. (Section 3, distributed acoustic sensing is used)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Koivisto and Azevedo with Wong as the references deal with simulation, in order to implement a system that uses distributed acoustic sensing. Wong would modify Koivisto and Azevedo by using distributed acoustic sensing. The benefit of doing so is proposed to detect and localize leakages in pipelines. (Wong Section 3)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Olauson et al. “Simulating intra-hourly wind power fluctuations on a power system level”: Also teaches creating blended data that is expressed as a power spectral density.
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/MICHAEL EDWARD COCCHI/Primary Examiner, Art Unit 2188
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