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 .
Election/Restrictions
Applicant’s election without traverse of Group II (14-17, 19-21, and 23) in the reply filed on 07/20/2026 is acknowledged.
Claims 1-10, 13, 26 and 38 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to nonelected inventions, there being no allowable generic or linking claim.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 14-17, 19-21 and 23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The 14 recites “the well treatment fluid” in lines 9-10. There is insufficient antecedent basis for this limitation in the claim. Note, claim 1, line 1 recites “well treatment fluids” which does not provide antecedent basis for “the well treatment fluid” in lines 9-10.
Claim 14, lines 16-17 recites “to characterize fluid properties….for the different size classes,” but should refer to the fluid properties of each of the well treatment fluid of each of the difference size classes.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
1. Claims 14-16, 19-21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2018/0223649 to Perkins et al. (cited by applicant) in view of U.S. Patent Application Publication No. 2007/0243523to Ionescu-Zanetti et al. (cited by applicant).
Perkins et al. discloses a system for characterizing well treatment fluids that involves in some embodiment, injecting a drilling mud into the at least one microfluidic channel, and the characteristic of the sample fluid is the content of an additive in the drilling mud. In yet other embodiments, injecting at least one of a solvent or a reagent into the microfluidic layer. In some embodiments, injecting the sample fluid into at least two microfluidic channels, providing a first illuminating light to a first one of the at least two microfluidic channels, and providing a second illuminating light to a second one of the at least two microfluidic channels. [0050]
The system includes a microfluidic chip (microfluidic optical computing device 300, Fig. 3A, microfluidic optical computing device 300, [0031], microfluidic chips ... optical computing device, [0014]).
The microfluidic chip includes at least one inlet (input channel 120) and a plurality of outlets (microfluidic channels 301b), and a channel array (microfluidic channels 301a) comprising plurality of microfluidic channels (microfluidic channels 301a), wherein the at least one inlet is in fluid communication (as shown in Fig. 3A) with the plurality of microfluidic channels (microfluidic channels 301a) wherein the plurality of microfluidic channels (microfluidic channels 301a) are in respective fluid communication with the plurality of outlets. (Fig. 3A; [0031]-[0033])
Perkins et al. teaches that a pump (sample fluid 150 is introduced...by a positive displacement force,[0033]) in fluid communication with the at least one inlet for passing the well treatment fluid (sample fluid 150) from input channel 120 into at least one of microfluidic channels 301 in microfluidic layer 304 ([0033]). Characteristics of the sample fluid include the content of an additive in the drilling mud. [0050]. The plurality of microfluidic channels partition the well treatment fluid (sample fluid 150).
An imaging sensor (photosensitive portion 312, Fig. 3A) induces a signal in driver circuit 320, which thereafter transmits the signal to controller 160. [0035]. Data is collected from microfluidic optical computing device 100 that is in optical communication with the microfluidic chip for obtaining imaging data of flows of the well treatment fluid in the plurality of microfluidic channels. [0046]
A computing system (at controller 160 that may include a processor 161 and a memory 162 storing instructions ([0019]) or a computerized system, ([0053]) and photosensitive portion 312 sends a signal to controller 160 ([0035]) for analyzing the imaging data (from photosensitive portion 312) Fig. 3A) to characterize fluid properties of the drilling mud including characteristics of the sample in a medium surrounding drill tool ([0046]) for the well treatment fluid (sample fluid 150) or for each of the different size classes.
Perkins et al does not teach that the plurality of microfluidic channels are a plurality of microfluidic channels having different cross-sectional dimensions, wherein the partition the well treatment fluid is partition well treatment fluid into a plurality of different size classes based on a cross-sectional dimension of a respective microfluidic channel.
Ionescu-Zanetti et al. is directed to a microfluidic device for analysis of individual particles in a suspension. [0022]
Ionescu-Zanetti et al. teaches a microfluidic device that includes a plurality of microfluidic channels {channels Q, Q/2, Q/4, Q/8, shown in Fig. 43A) in which flow in each channel is a fraction of the flow in the largest channel, Q ... Q, Q/2, Q/4 and Q/8. [0368]) The plurality of microfluidic channels have different cross-sectional dimensions (differing cross sectional branch dimensions [0368]), wherein fluid to be analyzed for suspended particles is partitioned (splits into a number of different fluid paths ([0368]) into a plurality of different size classes (each fluid path has a different fluidic resistance, imposed by differing cross sectional branch dimensions ([0368]) when measuring one or more characteristic of said particles ([0071] according to particle size distribution and classification of particles ([0180]) based on a cross-sectional dimension (differing cross sectional branch dimensions, [0368]) of a respective microfluidic channel.
It would have been obvious to one of ordinary skill in the art before applicant’s effective filing date to modify the plurality of microfluidic channels of Perkins et al. to include a plurality of microfluidic channels having different cross-sectional dimensions as taught by Ionescu-Zanetti et al. to partition well treatment fluid into a plurality of different size classes based on a cross-sectional dimension of a respective microfluidic channel as taught by Ionescu-Zanetti for purposes of providing different flow rates on portions of the well treatment fluid using different fluidic resistance and thereby ensure that microscope observation can be provided for different flow rates simultaneously as taught by Ionescu-Zanetti. [0368].
I.) As noted above Perkins et al. in view of Ionescu-Zanetti renders all the elements of claim 14.
Therefore, Perkins et al. in view of Ionescu-Zanetti et al. renders claim 14 obvious.
II.) Regarding applicant’s claim 15, as noted above, Perkins et al. in view of Ionescu-Zanetti et al. renders claim 14 obvious from which claim 15 depends.
Claim 15 recites one or more of: a pressure transducer coupled to the microfluidic chip for determining pressures in the plurality of microfluidic channels; a heater in thermal communication with the microfluidic chip for controlling a temperature in the plurality of microfluidic channels; or a light source in optical communication with the microfluidic chip for illuminating the plurality of microfluidic channels.
Perkins et al discloses a light source (light source layer 302, Fig. 3A) in optical communication (as shown in Fig. 3A) with the microfluidic chip (at microfluidic optical computing device 300) for illuminating the plurality of microfluidic channels (microfluidic channels 301a).
Therefore, Perkins et al. in view of Ionescu-Zanetti renders claim 15 obvious.
III.) Regarding applicant’s claim 16, as noted above, Perkins et al. in view of Ionescu-Zanetti et al. renders claim 14 obvious from which claim 16 depends.
Claim 16 recites that the computing system comprises a processor in data communication with the imaging sensor, and a non-transitory computer readable storage medium in data communication with the processor, the non-transitory computer readable storage medium comprising instructions that, when executed by the processor, cause the processor to perform operations including: obtaining imaging data of flows of the well treatment fluid in the plurality of microfluidic channels using the imaging sensor; and characterizing fluid properties for the well treatment fluid or for each of the different size classes using the imaging data.
Perkins et al. discloses that the computing system (controller 160, Fig. 1) comprises a processor (processor 161) and that imaging sensor (photosensitive portion 312) sends a signal to controller 160 ([0035]). The computing system includes non-transitory computer readable storage medium (memory 162, Fig. 1 storing instructions ([0019]), code stored on a non-transitory, computer-readable medium ([0053]) in data communication with the processor (processor 161). The non-transitory computer readable storage medium (memory 162) comprises instructions (memory 162 storing instructions ([0019]) that, when executed (processor configured to execute one or more sequences of instructions ([0053]) by the processor (processor 161), causes the processor (processor 161) to perform operations including: obtaining imaging data (from photosensitive portion 312, Fig. 3A) of flows of the well treatment fluid (sample fluid 150) in the plurality of microfluidic channels (microfluidic channels 301a) using the imaging sensor (photosensitive portion 312); and characterizing fluid properties (properties of the drilling mud ... characteristic of the sample [0046]) for the well treatment fluid (sample fluid 150) or for each of the different size classes using the imaging data (from photosensitive portion 312).
Therefore, Perkins in view of Ionescu-Zanetti et al. renders claim 16 obvious.
IV.) Regarding applicant’s claim 19, as noted above, Perkins et al. in view of Ionescu-Zanetti et al. renders claim 16 obvious from which claim 19 depends.
Claim 19 recites that characterizing the fluid properties comprises applying the imaging data as input to a trained machine-learning model for determining well treatment fluid properties.
Perkins et al. discloses characterizing the fluid properties (properties of the drilling mud ... characteristic of the sample [0046]) comprises applying the imaging data (from photosensitive portion 312, Fig. 3A) as input (signal to controller 160 [0035]) to a trained machine-learning model (an artificial neural network, or any like suitable entity that can perform calculations or other manipulations of data [0053]) for determining well treatment fluid (sample fluid 150) properties (properties of the drilling mud ... characteristic of the sample [0046]).
Therefore, Perkins et al. in view of Ionescu-Zanetti et al. renders claim 19 obvious.
V.) Regarding applicant’s claim 20, as noted above, Perkins et al. in view of Ionescu-Zanetti et al. renders claim 19 obvious from which claim 20 depends.
Claim 20 recites that the trained machine-learning model comprises: a set of parameters that were learned using a set of reference fluids, each reference fluid of the set of reference fluid corresponding to a previously characterized well treatment fluid or well treatment fluid component, and parameters of the set of parameters describing fluid properties of one or more reference fluids; and one or more functions configured to transform the input into predicted fluid properties using the set of parameters.
Perkins et al. discloses that the trained machine-learning model (an artificial neural network, or any like suitable entity that can perform calculations or other manipulations of data [0053]) comprises a set of parameters (a reference signal or a complementary signal from either one of ICE cores 306 ... a second characteristic of the sample that is different from the first characteristic of the sample [0037]) that were learned using a set of reference fluids (at ICE cores 306d, 306e, Fig. 3C, ICE cores 306d and 306e may be configured to form a reference signal [0037]), each reference fluid of the set of reference fluid (at ICE cores 306d, 306e) corresponding to a previously characterized well treatment fluid (sample fluid 150, Fig. 3A) or well treatment fluid component, and parameters of the set of parameters (a reference signal or a complementary signal from either one of ICE cores 306 ... a second characteristic of the sample that is different from the first characteristic of the sample [0037]) describing fluid properties (properties of the drilling mud ... characteristic of the sample [0046]) of one or more reference fluids (at ICE cores 306d, 306e, Fig. 3C); and one or more functions (can perform calculations or other manipulations of data, [0053]) configured to transform the input into predicted fluid properties (estimate the properties of the substance in real-time or near real-time [0030]) using the set of parameters (a reference signal or a complementary signal from either one of ICE cores 306 ... a second characteristic of the sample that is different from the first characteristic of the sample [0037]).
Therefore, Perkins et al. in view of Ionescu-Zanetti et al. renders claim 20 obvious.
VI.) Regarding applicant’s claim 21, as noted above, Perkins et al. in view of Ionescu-Zanetti et al. renders claim 19 obvious from which claim 21 depends.
Claim 21 recites that the trained machine-learning model generates outputs comprising the fluid properties for the well treatment fluid or for each of the different size classes, or wherein the input comprises one or more of: pressures at the plurality of microfluidic channels, temperature dependent imaging data, or pressure dependent imaging data.
Perkins et al. discloses that the trained machine-learning model (an artificial neural network, or any like suitable entity that can perform calculations or other manipulations of data [0053]) generates outputs (measured values [0052]) comprising the fluid properties (properties of the drilling mud ... characteristic of the sample [0046 ]) for the well treatment fluid (sample fluid 150, Fig. 3A) or for each of the different size classes.
Therefore, Perkins et al. in view of Ionescu-Zanetti et al. renders claim 21 obvious.
VII.) Regarding applicant’s claim 23, as noted above, Perkins et al. in view of Ionescu-Zanetti et al. renders claim 14 obvious from which claim 23 depends.
Claim 23 recites that the well treatment fluid comprises drilling mud, a cleaning fluid, a casing fluid, or a reservoir fluid, wherein the fluid properties comprises one or more rheological properties, one or more chemical properties, or one or more physical properties, or wherein the fluid properties comprises one or more of a particle size, a size distribution of particles, an alkalinity, a viscosity, or an opacity.
Perkins et al. discloses that the well treatment fluid (sample fluid 150, Fig. 3A) comprises drilling mud (sample fluid ... drilling mud [0050], physical or chemical properties of the drilling mud [0046]), a cleaning fluid, a casing fluid, or a reservoir fluid.
Perkins et al. teaches that Illustrative characteristics of a substance that can be monitored with the optical computing devices include impurity content, pH, alkalinity, viscosity, density, ionic strength, total dissolved solids, salt content (e.g., salinity), porosity, opacity, bacteria content, total hardness, combinations thereof, state of matter. [0015]
Therefore, Perkins et al. in view of Ionescu-Zanetti et al. renders claim 23 obvious.
2. Claim 17 is rejected under 35 USC 103 as being unpatentable over Perkins et al. in view of Ionescu-Zanetti as applied to claim 14 and further in view of U.S. Patent Application Publication No. 2020/0041413 to Palanisami et al. (cited by applicant)
I.) Regarding applicant’s claim 17, as noted above Perkins et al. in view of Ionescu-Zanetti renders claim 14 obvious from which alim 17 depends.
Claim 17 recites that the operations further include controlling a temperature of the plurality of microfluidic channels using a heater to obtain temperature dependent imaging data of flows of the well treatment fluid in the plurality of microfluidic channels, wherein characterizing the fluid properties comprises using the temperature dependent imaging data, or wherein the operations further include monitoring pressures at the plurality of microfluidic channels using a pressure transducer while obtaining the imaging data, wherein characterizing the fluid properties comprises using the imaging data and the pressures.
Perkins et al. in view of Ionescu-Zanetti et al. teaches using imaging data, but does not teach that the operations further include controlling a temperature of the plurality of microfluidic channels using a heater to obtain temperature dependent imaging data of flows of the well treatment fluid in the plurality of microfluidic channels, wherein characterizing the fluid properties comprises using the temperature dependent imaging data, or wherein the operations further include monitoring pressures at the plurality of microfluidic channels using a pressure transducer while obtaining the imaging data.
Palanisami et al. teaches performing fluoroscopy ([0002]) and discloses controlling (through heating device 106, Fig. 1A, heating device 106 can be any type of device that supplies heat to maintain a certain temperature (0058]) a temperature (temperature [0058]) of a plurality of microfluidic channels (channels 248, 254, Fig. 2A) using a heater (heating device 106, Fig. 1A) to obtain temperature dependent imaging data (from camera 112) of flows of a well treatment fluid (materials (e.g., liquid, target analytes, and/or reagents) can pass through the fluidic circuit [0093]) in the plurality of microfluidic channels (channels 248, 254, Fig. 2A), wherein characterizing fluid properties (camera 112 ... capable of capturing images that represent a fluorescence signal produced by the detection reagent within the microfluidic device 102 [0063]) comprises using the temperature dependent imaging data (from camera 112, Fig. 1A).
It would have been obvious to one of ordinary skill in the art before applicant’ effective filing date to modify Perkins et al. in view of Ionescu-Zanetti et al. to modify the plurality of microfluidic channels to include controlling temperature of the plurality of microfluidic channels using a heater to obtain temperature dependent imaging data of flows of the well treatment fluid in the plurality of microfluidic channels, wherein characterizing the fluid properties comprises using the temperature dependent imaging data for purposes of adjusting the amount of heat supplied to the microfluidic device and thereby ensure that the amount of heat can be adjusted based on the type of fluorometric assay to be performed (Palanisami et al. [0059]).
Therefore, Perkins et al. in view of Ionescu-Zanetti and Palanisami et al. renders claim 17 obvious.
Conclusion
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/MICHAEL STANLEY GZYBOWSKI/Examiner, Art Unit 1798
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