DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 3-11, and 13-20 have been considered but are moot in view of the new grounds of rejection necessitated by the applicant’s amendments to the claims.
The applicant’s amendments to the claims have necessitated the below 35 USC 112 and 101 rejections.
The 35 USC 103 rejection utilizes the same art, but new explanations are given below, in view of the applicant’s amendments to the claims.
Specification and Drawings
As stated in a previous action, the applicant’s amended specification and drawings of 09/14/21 are accepted.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-11, and 13-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 11, and 20 have been amended to include the following limitations:
computing/compute, at the one or more processors, an uncertainty measure associated with the first prediction and determining that the uncertainty measure satisfies a predefined threshold
responsive to determining that the uncertainty measure satisfies the predefined threshold, providing one or more input properties derived from the one or more measurements to an adaptive inference system to generate a second prediction of the composition characterization of the downhole fluid, wherein the adaptive inference system is trained on fluid data obtained from a database and maps input fluid properties to input functions related to the input fluid properties
The examiner could not find support in the applicant’s disclosure for these amendments.
The word “threshold” was not found in the applicant’s original specification, let alone any disclosure of computing an uncertainty measure associated with the first prediction and determining that the uncertainty measure satisfies a predefined threshold.
The closest support for this limitation that the examiner could find in the applicant’s original specification was in paragraph 0053, where it was stated:
In other examples, a processor can determine which properties of the first prediction need to be enhanced by the second processor. Those properties that need enhancement can be called low-uncertainty inputs. The processor determines which of the properties of the prediction the low-uncertainty inputs are by considering many factors such as the sum of the density components, the ratio of SARA components, and other suitable factors. Once the low-uncertainty inputs are determined, the second processor uses the ANFIS to calculate a second prediction of the low-uncertainty inputs to enhance the first prediction. The second prediction can be stored on a non-transitory computer-readable storage medium. The second prediction can be transmitted uphole. In at least one example, the second prediction can be stored on a non-transitory computer-readable storage medium uphole.
This section does not mention a threshold, let alone any disclosure of computing an uncertainty measure associated with the first prediction and determining that the uncertainty measure satisfies a predefined threshold. The examiner is not sure if the applicant is inferring that some type of threshold must be inherent in the many factors that are considered in the prediction of the low-uncertainty inputs, such as the sum of the density components, the ratio of SARA components, and other suitable factors.
The examiner does not consider paragraph 0053 to be suitable support for the applicant’s amended limitations. For the purposes of examination however, the examiner will interpret any teachings of “uncertainty” to anticipate the claimed limitation of “computing, at the one or more processors, an uncertainty measure associated with the first prediction and determining that the uncertainty measure satisfies a predefined threshold.”
The paragraph in the applicant’s original specification that appeared to provide the most support for the amended limitation of “responsive to determining that the uncertainty measure satisfies the predefined threshold … and maps input fluid properties to input functions related to the input fluid properties …” was paragraph 0016, which stated:
After the measurements are taken by the fluid sensors, one or more processors generates a first prediction of the properties of the downhole fluid in real time using the measurements from the fluid sensors. The processor can use, for example, a neural network ensemble. To enhance the first prediction, the second processor, also in real time, uses an adaptive neuro-fuzzy inference system (ANFIS) to generate a second prediction of the properties based on the first prediction. To supplement and train the ANFIS, fluid data which is stored on a non-transitory computer-readable storage medium is utilized. The fluid data can be obtained from an optical-PVT (pressure-volume-temperature) database. The overall characterization of the downhole fluid is thus enhanced without post-processing of the measurements obtained from the fluid sensors.
This section also does not mention a predefined threshold, let alone some sort of action that is responsive to determining that the uncertainty measure satisfies the predefined threshold. Here, it is disclosed that the adaptive inference system is an adaptive neuro-fuzzy inference system (ANFIS). For the purposes of examination, the examiner will interpret any art that teaches ANFIS to anticipate this limitation.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-11, and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to a method, which is a process. Independent claim 11 is directed to a fluid analysis tool, which is a machine. Independent claim 20 is directed to a non-transitory computer-readable storage medium, which is a manufacture. All other claims depend on independent claims 1, 11, and 20. As such, claims 1, 3-11, and 13-20 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
generating, using a first predictive model, a first prediction of a composition characterization based on the one or more measurements (This limitation recites an abstract idea in the form of mathematical concepts. Paragraph 0002 of the applicant’s 01/29/19 specification states, “Using the determined properties from the optical sensors, models such as neural network ensembles can provide predictions of the formation fluid.” Neural network ensembles are mathematical models.)
computing an uncertainty measure associated with the first prediction and determining that the uncertainty measure satisfies a predefined threshold (Computing an uncertainty measure is a mathematical calculation. It therefore recites an abstract mathematical concept. Determining that an uncertainty measure satisfies a predefined threshold is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. It therefore recites an abstract mental process.)
responsive to determining that the uncertainty measure satisfies the predefined threshold, providing one or more input properties derived from the one or more measurements to an adaptive inference system to generate a second prediction of the composition characterization of the downhole fluid, wherein the adaptive inference system is trained on fluid data obtained from a database and maps input fluid properties to input functions related to the input fluid properties (This limitation recites abstract mathematical formulas and calculations. The applicant’s specification explicitly defines the ANFIS model structure in the context of specific equations, such as equations (1) – (5) in paragraphs 0038, 0041, and 0043-0044 of the applicant’s original specification.)
selecting the second prediction as the composition characterization of the downhole fluid (Making a simple selection is an evaluation that can be performed in the human mind. The limitation therefore recites an abstract mental process.)
Claim 11
generate, using a first predictive model, a first prediction of a composition characterization based on one or more measurements associated with one or more properties (recites an abstract idea for the reasons discussed with respect to claim 1 above)
compute an uncertainty measure associated with the first prediction and determining that the uncertainty measure satisfies a predefined threshold (recites an abstract idea for the reasons discussed with respect to claim 1 above)
responsive to determining that the uncertainty measure satisfies the predefined threshold, providing one or more input properties derived from the one or more measurements to an adaptive inference system to generate a second prediction of the composition characterization of the downhole fluid, wherein the adaptive inference system is trained on fluid data obtained from a database and maps input fluid properties to input functions related to the input fluid properties (recites an abstract idea for the reasons discussed with respect to claim 1 above)
select the second prediction as the composition characterization of the downhole fluid (recites an abstract idea for the reasons discussed with respect to claim 1 above)
Claim 20
generating, using a first predictive model, a first prediction of a composition characterization based on the one or more measurements (recites an abstract idea for the reasons discussed with respect to claim 1 above)
computing an uncertainty measure associated with the first prediction and determining that the uncertainty measure satisfies a predefined threshold (recites an abstract idea for the reasons discussed with respect to claim 1 above)
responsive to determining that the uncertainty measure satisfies the predefined threshold, providing one or more input properties derived from the one or more measurements to an adaptive inference system to generate a second prediction of the composition characterization of the downhole fluid, wherein the adaptive inference system is trained on fluid data obtained from a database and maps input fluid properties to input functions related to the input fluid properties (recites an abstract idea for the reasons discussed with respect to claim 1 above)
selecting the second prediction as the composition characterization of the downhole fluid (Making a simple selection is an evaluation that can be performed in the human mind. The limitation therefore recites an abstract mental process.)
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claim 1
A method comprising: obtaining, at one or more fluid sensors of a fluid analysis tool in a wellbore, one or more measurements associated with one or more properties of a downhole fluid, the one or more fluid sensors communicatively coupled with one or more processors (Although this limitation recites different structural elements (such as fluid sensors and processors), the limitation is not indicative of integration into a practical application because it generally links the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Here, the focus of the claim is on data processing using abstract mathematical concepts, and this limitation merely serves to gather the data used for the abstract data processing. There is no application or using of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, communicatively coupling the fluid sensors to the one or more processors merely use a computer as a tool to perform an abstract idea, which is not indicative of integration into a practical application (see MPEP 2106.05(f)).)
at the one or more processors (This phrase merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). It is not indicative of integration into a practical application.)
of the downhole fluid (This phrase merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). It is not indicative of integration into a practical application.)
storing the composition characterization on one or more non-transitory computer-readable storage media (A non-transitory computer-readable storage media is a structural element. However, this limitation merely uses a computer as a tool to perform an abstract idea. In addition, generically storing data on a non-transitory computer-readable storage media can be considered insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)), which is not indicative of integration into a practical application. Also, storing data on one or more non-transitory computer readable storage media merely uses a computer as a tool to perform an abstract idea.) as a characterization of the downhole fluid while refraining from post-processing the measurement of the one or more properties to generate the characterization of the downhole fluid (This limitation appears to be merely descriptive of the nature of the second prediction data output; it is describing the second prediction as “a characterization of the downhole fluid …” Merely describing the nature of a data result is not indicative of integration into a practical application. It merely serves to generally link the use of the judicial exception to a particular technological environment or field of use. It is not indicative of integration into a practical application.)
Independent claims 11 and 20 recite similar limitations that are not indicative of integration into a practical application.
Claims 3 and 13
wherein the first prediction and the second prediction are generated in real time (Paragraph 0016 of the applicant’s specification supports this limitation by stating, “one or more processors generates a first prediction of the properties of the downhole fluid in real time … the second processor, also in real time …” In view of this disclosure, the disclosure of “real time” is a product of using a computer as a tool to perform an abstract idea. This is not indicative of integration into a practical application.)
Claims 4 and 14
wherein the one or more fluid sensors are optical sensors (This limitation generally links the use of the judicial exception to a particular technological environment. It is not indicative of integration into a practical application.)
Claims 5 and 15
wherein the optical sensors are analyte specific broadband filters (This limitation generally links the use of the judicial exception to a particular technological environment. It is not indicative of integration into a practical application.)
Claims 6 and 16
wherein the one or more properties are selected from one or more of C1-C5 hydrocarbon concentration, gas oil ratio, sum of SARA (saturates, aromatics, resins, and asphaltenes) concentration, positive correlated saturates, and negative correlated saturates (This limitation links data to be processed with generic properties. It generally links the use of the judicial exception to a particular technological environment or field of use. It is not indicative of integration into a practical application.)
Claims 7 and 17
wherein the one or more non-transitory computer-readable storage media has instructions stored which are executed by the one or more processors, wherein the one or more non-transitory computer-readable media stores fluid data, wherein the ANFIS of the one or more processors generates the second prediction with the fluid data from the one or more non-transitory computer-readable storage media and the first prediction (This limitation merely uses a computer as a tool to perform an abstract idea. It is not indicative of integration into a practical application.)
Claims 8 and 18
wherein the one or more non-transitory computer-readable storage media has instructions stored which are executed by the one or more processors, wherein the one or more non-transitory computer-readable media stores fluid data, wherein the ANFIS of the one or more processors generates the second prediction with the fluid data from the one or more non-transitory computer-readable storage media and the measurement of the one or more properties from the one or more fluid sensors (This limitation merely uses a computer as a tool to perform an abstract idea. It is not indicative of integration into a practical application.)
Claims 9 and 19
receiving, via the one or more non-transitory computer-readable storage media from the one or more processors, data comprising the first prediction (This limitation merely uses a computer as a tool to perform an abstract idea. It is not indicative of integration into a practical application.)
storing, via the one or more non-transitory computer-readable storage media, the data comprising the first prediction (This limitation merely uses a computer as a tool to perform an abstract idea. It is not indicative of integration into a practical application.)
Claim 10
further comprising: transmitting the second prediction uphole (This limitation merely uses a computer as a tool to perform an abstract idea (the computer transmits data). It is not indicative of integration into a practical application.)
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving data, processing data, and outputting a result based on the processed data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
For the above reasons, claims 1, 3-11, and 13-20 do not qualify as eligible subject matter under 35 U.S.C. 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, 3-4, 6-11, 13-14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hegeman et al (US PgPub 20090030858) in view of Gabralla et al NPL (Gabralla, Lubna A.; Wahby, Talaat M.; Ojha, Varun K.; and Abraham, Ajisth. (2014). Ensemble of Adaptive Neuro-Fuzzy Inference System Using Particle Swarm Optimization for Prediction of Crude Oil Prices. 2014 IEEE, 141-146.).
With respect to claim 1, Hegeman et al discloses:
A method (abstract)
obtaining, at one or more fluid sensors of a fluid analysis tool in a wellbore, one or more measurements associated with one or more properties of a downhole fluid, the one or more fluid sensors communicatively coupled with one or more processors (figure 1, reference 125; paragraph 0033 states, “Sensor(s) of the fluid analyzer module 125 may provide measurements as the fluid is being pumped … In the case of optical sensors (e.g., a spectrometer), optical densities received from the optical sensors may be used to compute a formation fluid composition.” See also figure 3 and paragraph 0044, which states, “In some implementations, the example methods and apparatus described herein may be implemented using a permanent downhole sensor tool.” Figure 1 shows an example controller and/or processing system 118, and figure 3 shows a data processor 254. Based on the disclosure, it is clear that the sensors are communicatively coupled to these processors. For example, paragraph 0044 states, “The example wireline tool 250 is provided with one or more sensor(s) 252 … used to measure fluid properties that can be communicated uphole to a data processor 254 and used to predict or estimate PVT fluid properties as described herein.”)
generating, at the one or more processors using a first predictive model, a first prediction of a composition characterization of the downhole fluid based on the one or more measurements (abstract states, “Apparatus and methods to perform downhole fluid analysis using an artificial neural network are disclosed.”; paragraph 0024 states, “the methods and apparatus described herein may be used to predict or estimate PVT fluid properties used as input to thermodynamic models of reservoirs …”; paragraph 0044 states, “used to measure fluid properties that can be communicated uphole to a data processor 254 and used to predict or estimate PVT fluid properties …” (emphasis mine).; see also paragraphs 0025, 0047, 0054, 0061, 0064-0065, and 0082 for teachings of “models.”)
computing, at the one or more processors, an uncertainty measure associated with the first prediction and determining that the uncertainty measure satisfies a predefined threshold (paragraph 0024 states, “the example methods and apparatus described herein may be used to generate uncertainty estimates indicative of the accuracy of the predicted fluid properties.”; Please note that Hegeman et al discloses “uncertainty” throughout its disclosure, and it mentions uncertainty many more times than the applicant’s own specification. As some examples of further uncertainty teachings, please note paragraphs 0064-0066, 0077, and 0089.)
With respect to claim 1, Hegeman et al differs from the claimed invention in that it does not explicitly disclose:
responsive to determining that the uncertainty measure satisfies the predefined threshold, providing one or more input properties derived from the one or more measurements to an adaptive inference system to generate a second prediction of the composition characterization of the downhole fluid, wherein the adaptive inference system is trained on fluid data obtained from a database and maps input fluid properties to input functions related to the input fluid properties
selecting the second prediction as the composition characterization of the downhole fluid
storing the composition characterization on one or more non-transitory computer-readable storage media
With respect to claim 1, Gabralla et al NPL discloses:
responsive to determining that the uncertainty measure satisfies the predefined threshold, providing one or more input properties derived from the one or more measurements to an adaptive inference system to generate a second prediction of the composition characterization of the downhole fluid, wherein the adaptive inference system is trained on fluid data obtained from a database and maps input fluid properties to input functions related to the input fluid properties (As discussed in the 112 rejection above, “For the purposes of examination, the examiner will interpret any art that teaches ANFIS to anticipate this limitation.” Page 1, column 2, paragraph 1 of Gabralla et al NPL states, “This research uses an ensemble method for ANFIS, which is a fuzzy inference system fine tuned using neural network learning methods to improve the accuracy …”; Gabralla et al teaches that ensemble methods (i.e. combining a first learning model with ANFIS) can produce better results.)
selecting the second prediction as the composition characterization of the downhole fluid (obvious in view of combination; Hegeman et al discloses composition characterization of the downhole fluid (paragraph 0027). Gabralla et al discloses what can be construed as a second prediction that results from refining a first prediction.)
storing the composition characterization on one or more non-transitory computer-readable storage media (obvious in view of combination; paragraph 0029 of Hegeman states, “may include one or more microprocessors or other processors or processing units, associated memory, and other hardware and/or software.”)
With respect to claim 1, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Gabralla et al NPL into the invention of Hegeman et al. The motivation for the skilled artisan in doing so is to gain the benefit of more accurate and reliable predictions.
With respect to claim 11, Hegeman et al discloses:
A fluid analysis tool (abstract; figures 1-2)
one or more fluid sensors (paragraphs 0030 and 0033)
one or more processors communicatively coupled with the one or more fluid sensors (paragraphs 0029, 0044, and 0071)
one or more non-transitory computer-readable storage media having instructions stored which when executed by the one or more processors, cause the one or more processors to: (paragraph 0071 state, “Additionally or alternatively, some or all of the blocks of the example apparatus 2000 or parts thereof, may be implemented using instructions, code, and/or other software and/or firmware, etc. stored on a machine accessible medium that, when executed by, for example, a processor system … perform the operations …”)
generate, using a first predictive model, a first prediction of a composition characterization of a downhole fluid based on one or more measurements associated with one or more properties of the downhole fluid (see rejection of claim 1 above)
compute, at the one or more processors, an uncertainty measure associated with the first prediction and determining that the uncertainty measure satisfies a predefined threshold (see rejection of claim 1 above)
With respect to claim 11, Hegeman et al differs from the claimed invention in that it does not explicitly disclose:
responsive to determining that the uncertainty measure satisfies the predefined threshold, providing one or more input properties derived from the one or more measurements to an adaptive inference system to generate a second prediction of the composition characterization of the downhole fluid, wherein the adaptive inference system is trained on fluid data obtained from a database and maps input fluid properties to input functions related to the input fluid properties
select the second prediction as the composition characterization of the downhole fluid
store the composition characterization on one or more non-transitory computer-readable storage media
With respect to claim 11, Gabralla et al NPL discloses:
responsive to determining that the uncertainty measure satisfies the predefined threshold, providing one or more input properties derived from the one or more measurements to an adaptive inference system to generate a second prediction of the composition characterization of the downhole fluid, wherein the adaptive inference system is trained on fluid data obtained from a database and maps input fluid properties to input functions related to the input fluid properties (see rejection of claim 1 above)
select the second prediction as the composition characterization of the downhole fluid (see rejection of claim 1 above)
store the composition characterization on one or more non-transitory computer-readable storage media (see rejection of claim 1 above)
With respect to claim 11, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Gabralla et al NPL into the invention of Hegeman et al. The motivation for the skilled artisan in doing so is to gain the benefit of more accurate and reliable predictions.
With respect to claim 20, Hegeman et al discloses:
A non-transitory computer-readable storage medium having instructions stored which, when executed by a computing device, cause the computing device to perform operations (paragraph 0071)
obtaining, at one or more fluid sensors of a fluid analysis tool in a wellbore, one or more measurements associated with one or more properties of a downhole fluid, the one or more fluid sensors communicatively coupled with one or more processors (figure 1, reference 125; paragraph 0033 states, “Sensor(s) of the fluid analyzer module 125 may provide measurements as the fluid is being pumped … In the case of optical sensors (e.g., a spectrometer), optical densities received from the optical sensors may be used to compute a formation fluid composition.” See also figure 3 and paragraph 0044, which states, “In some implementations, the example methods and apparatus described herein may be implemented using a permanent downhole sensor tool.” Figure 1 shows an example controller and/or processing system 118, and figure 3 shows a data processor 254. Based on the disclosure, it is clear that the sensors are communicatively coupled to these processors. For example, paragraph 0044 states, “The example wireline tool 250 is provided with one or more sensor(s) 252 … used to measure fluid properties that can be communicated uphole to a data processor 254 and used to predict or estimate PVT fluid properties as described herein.”)
generating, at the one or more processors using a first predictive model, a first prediction of a composition characterization of the downhole fluid based on the one or more measurements (see rejection of claim 1 above)
computing, at the one or more processors, an uncertainty measure associated with the first prediction and determining that the uncertainty measure satisfies a predefined threshold (see rejection of claim 1 above)
With respect to claim 20, Hegeman et al differs from the claimed invention in that it does not explicitly disclose:
responsive to determining that the uncertainty measure satisfies the predefined threshold, providing one or more input properties derived from the one or more measurements to an adaptive inference system to generate a second prediction of the composition characterization of the downhole fluid, wherein the adaptive inference system is trained on fluid data obtained from a database and maps input fluid properties to input functions related to the input fluid properties
selecting the second prediction as the composition characterization of the downhole fluid
storing the composition characterization on one or more non-transitory computer-readable storage media
With respect to claim 20, Gabralla et al NPL discloses:
responsive to determining that the uncertainty measure satisfies the predefined threshold, providing one or more input properties derived from the one or more measurements to an adaptive inference system to generate a second prediction of the composition characterization of the downhole fluid, wherein the adaptive inference system is trained on fluid data obtained from a database and maps input fluid properties to input functions related to the input fluid properties (see rejection of claim 1 above)
selecting the second prediction as the composition characterization of the downhole fluid (see rejection of claim 1 above)
storing the composition characterization on one or more non-transitory computer-readable storage media (see rejection of claim 1 above)
With respect to claim 20, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Gabralla et al NPL into the invention of Hegeman et al. The motivation for the skilled artisan in doing so is to gain the benefit of even more accurate and reliable predictions.
With respect to claims 3 and 13, Hegeman et al, as modified, discloses:
wherein the first prediction and the second prediction are generated in real time (obvious in view of combination; Paragraph 0024 of Hegeman states, “Predicted values may also be advantageously used in real time …” Paragraph 0026 of Hegeman states, “so that the ANN can be used during downhole measurement processes to determine downhole fluid properties of extracted formation fluid samples in real time.”)
With respect to claims 4 and 14, Hegeman et al, as modified, discloses:
wherein the one or more fluid sensors are optical sensors (See paragraph 0033 of Hegeman.)
With respect to claims 6 and 16, Hegeman et al, as modified, discloses:
wherein the one or more properties are selected from one or more of C1-C5 hydrocarbon concentration … (see paragraphs 0072 and 0080 of Hegeman)
With respect to claims 7 and 17, Hegeman et al, as modified, discloses:
wherein the one or more non-transitory computer-readable storage media has instructions stored which are executed by the one or more processors; wherein the one or more non-transitory computer-readable storage media stores the fluid data, wherein the ANFIS of the one or more processors generates the second prediction with the fluid data from the one or more non-transitory computer-readable storage media and the first prediction (obvious in view of combination; Hegeman discloses memory and processors and various use cases for them (see, for example, paragraphs 0029, 0041-0042, 0044, 0071, 0073, 0081-0083, and 0088). Reading various types of data and instructions into and out of non-transitory computer-readable storage media is obvious to one of ordinary skill in the art.)
With respect to claims 8 and 18, Hegeman et al, as modified, discloses:
wherein the one or more non-transitory computer-readable media has instructions stored which are executed by the one or more processors; wherein the one or more non-transitory computer-readable storage media stores the fluid data; wherein the ANFIS of the one or more processors generates the second prediction with the fluid data from the one or more non-transitory computer-readable storage media and the measurement of the one or more properties from the one or more fluid sensors (obvious in view of combination; Hegeman discloses memory and processors and various use cases for them (see, for example, paragraphs 0029, 0041-0042, 0044, 0071, 0073, 0081-0083, and 0088). Reading various types of data and instructions into and out of non-transitory computer-readable storage media is obvious to one of ordinary skill in the art.)
With respect to claims 9 and 19, Hegeman et al, as modified, discloses:
receiving, via the one or more non-transitory computer-readable storage media from the one or more processors, data comprising the first prediction (obvious in view of combination; Hegeman discloses memory and processors and various use cases for them (see, for example, paragraphs 0029, 0041-0042, 0044, 0071, 0073, 0081-0083, and 0088). Reading various types of data and instructions into and out of non-transitory computer-readable storage media is obvious to one of ordinary skill in the art.)
storing, via the one or more non-transitory computer-readable storage media, the data comprising the first prediction (obvious in view of combination; Hegeman discloses memory and processors and various use cases for them (see, for example, paragraphs 0029, 0041-0042, 0044, 0071, 0073, 0081-0083, and 0088). Reading various types of data and instructions into and out of non-transitory computer-readable storage media is obvious to one of ordinary skill in the art.)
With respect to claim 10, Hegeman et al, as modified, discloses:
further comprising: transmitting the second prediction uphole (obvious in view of combination; As stated above, paragraph 0044 of Hegeman et al discloses, “used to measure fluid properties that can be communicated uphole to a data processor 254 …” The secondary art teaches the second prediction.)
Claims 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hegeman et al (US PgPub 20090030858) in view of Gabralla et al NPL (Gabralla, Lubna A.; Wahby, Talaat M.; Ojha, Varun K.; and Abraham, Ajisth. (2014). Ensemble of Adaptive Neuro-Fuzzy Inference System Using Particle Swarm Optimization for Prediction of Crude Oil Prices. 2014 IEEE, 141-146.), as applied to claims 1, 3-4, 6-11, 13-14, and 16-20 above, and further in view of Perkins et al (US Pat 9851340).
With respect to claims 5 and 15, Hegeman et al, as modified, discloses:
The method of claim 4 (as applied to claim 4 above)
The fluid analysis tool of claim 14 (as applied to claim 14 above)
With respect to claims 5 and 15, Hegeman et al, as modified, differs from the claimed invention in that it does not explicitly disclose:
wherein the optical sensors are analyte specific broadband filters
With respect to claims 5 and 15, Perkins et al discloses:
wherein the optical sensors are analyte specific broadband filters (obvious in view of combination; Paragraph 0017 of the applicant’s specification states, “The fluid sensors 52 can be optical sensors. In other examples, the fluid sensors 52 can be analyte specific broadband filters, for example HALLIBURTON® ICE CORE® sensors.” The abstract of Perkins et al discloses, “In some implementations, optical analysis systems use an integrated computational element (ICE) that includes a planar waveguide configured as an ICE core.” Furthermore, under the OTHER PUBLICATIONS section on page 2, Perkins et al discloses two 2013 references that teach ICE Core. In view of the applicant’s specification, the examiner will construe the disclosure of ICE Core to anticipate the claimed “analyte specific broadband filters,” since the applicant’s specification states that the HALLIBURTON ICE CORE sensors are an example of analyte specific broadband filters. As discussed above, Hegeman discloses optical sensors (paragraph 0033). It would be obvious to one of ordinary skill in the art to use ICE core sensors in place of the optical sensors, since the ICE core sensors appear to be an obvious replacement. The rationale for doing so is design incentives or market forces prompting variations. The prior art teaches a base device similar or analogous to the claims. Design incentives or market forces would have prompted change to the base device. Known variations or principles would meet the difference between the claimed invention and the prior art and the implementation would have been predictable.)
With respect to claims 5 and 15, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Perkins et al into the invention of modified Hegeman et al. The motivation for the skilled artisan in doing so is to gain the benefit of identifying the presence and proportions of specific fluid components.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huber et al (US Pat 6151961) discloses downhole depth correlation.
Dasys et al (US PgPub 20190226314) discloses explorative sampling of natural mineral resource deposits.
Etkin et al (US PgPub 20200401938) discloses machine learning based generation of ontology for structural and functional mapping.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM.
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/LEONARD S LIANG/Examiner, Art Unit 2857 02/03/26
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857