Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-7 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.
On lines 13-14 of claim 1, the phrase “determining a concentration of water in the liquid medium based on the detected water droplets” is indefinite since no liquid medium has been positively recited in any of the components of the system. Is a liquid medium located in a pipe to which the flow cell is fluidly coupled, and does the liquid medium flow through the flow cell so as to pass by the light source and infrared camera in order to obtain one or more images, detect water droplets in the one or more images using a trained machine learning model, and determine a concentration of water in the liquid medium based on the detected water droplets?
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 3, 8, 10, 13, 15, 17 and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, 10, 13, 15, and 17 of copending Application No. 19/178,253 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims recite a system for measuring water concentration in a liquid medium (see instant claims 1 and claims 1 in application 19/178,253), wherein the system comprises a flow cell configured to couple to a pipe, a light source coupled to a first side of the flow cell, an infrared camera coupled to a second side of the flow cell opposite the first side, and at least one processor and a memory storing instructions that when executed by the at least one processor cause the at least one processor to acquire one or more images captured by the infrared camera, detect water droplets in the one or more images using a trained machine learning model, and determine a concentration of water in the liquid medium based on the detected water droplets (see instant claim 1 and claim 1 in application 19/178,253). Both sets of claims also recite that the trained machine learning model in the system comprises a trained recurrent neural network (see instant claim 3 and claim 1 in application 19/178,253). Both sets of claims also recite a method for measuring water concentration in a liquid medium comprising acquiring one or more images captured by an infrared camera coupled to a flow cell comprising a liquid medium, detecting water droplets in the one or more images using a trained machine learning model, and determining a concentration of water in the liquid medium based on the detected water droplets (see instant claim 8 and claim 8 in application 19/178,253). Both sets of claims also recite that the trained machine learning model in the system comprises a trained recurrent neural network (see instant claim 10 and claim 8 in application 19/178,253). Both sets of claims also recite training the machine learning model based on images acquired from the infrared camera (see instant claim 13 and claim 10 in application 19/178,253). Both sets of claims also recite generating an alert when a determined water concentration exceeds a threshold water concentration (see instant claim 15 and claim 13 in application 19/178,253). Both sets of claims also recite one or more non-transitory machine readable storage devices storing instructions for measuring water concentration in a liquid medium, the instructions being executable by one or more processors to cause operations comprising acquiring one or more images captured by an infrared camera coupled to a flow cell comprising a liquid medium, detecting water droplets in the one or more images using a trained machine learning model, and determining a concentration of water in the liquid medium based on the detected water droplets (see instant claim 17 and claim 15 in application 19/178,253). Both sets of claims also recite that the operations performed by the one or more processors further comprise training the machine learning model based on images acquired from the infrared camera (see instant claim 20 and claim 17 in application 19/178,253
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Inventorship
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.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Truong et al (US 2022/0288523, submitted in the IDS filed on February 5, 2025) in view of CN 202372439, also submitted in the IDS filed on February 5, 2025).
With regards to claims 1-2, Truong et al teach of a system 100 for measuring a concentration of a component in a liquid medium, such as the concentration of a hydrocarbon or oil in a water stream (see paragraphs 0040 and 0070-0073 in Truong et al). The system 100 comprises a flow cell 142, a light source 144 coupled to a first side of the flow cell 142, and an infrared camera 146 coupled to a second side of the flow cell 142 opposite the first side (see Figure 1, paragraph 0048 in Truong et al where it states:
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Also, see paragraph 0055 in Truong et al where it states:
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The system taught by Troung et al also comprises at least one processor and a memory storing instructions (see paragraph 0059 in Truong et al) that when executed by the at least one processor cause the at least one processor to perform operations comprising acquiring one or more images from the infrared camera, detecting hydrocarbon or oil droplets in a water stream in the one or more images using a trained learning model, and determining a concentration of hydrocarbon or oil in the water stream based on the detected hydrocarbon or oil droplets. See paragraph 0057 in Truong et al where it states:
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Also, see paragraphs 0072-0073 in Truong et al where it states:
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Also, see paragraphs 0082-0083 in Truong et al where it states:
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Truong et al fail to teach that the system can be used to measure a water concentration in a liquid medium such as a hydrocarbon/oil condensate or a natural gas liquid, and that the flow cell 142 is configured to be coupled to a pipe carrying the liquid medium.
CN202372439 teaches of a system and a method for measuring water content in a liquid medium such as crude oil. The system comprises a light source 201 that emits infrared light towards a first side of a flow cell 205 that is coupled to a pipe 301 carrying a crude oil liquid medium, wherein the flow cell 205 is coupled to the pipe 301 using pipe flange connections 302, an infrared camera 209 coupled to a second side of the flow cell 205 that is opposite to the first side, and a computing machine that analyzes the images acquired by the infrared camera 209 to detect water droplets in the crude oil and determine a concentration of water in the crude oil based on the detected water droplets. See Figure 2, paragraphs 0009-0010 and 0012-0013, and clams 1-2 in the English-language translation of CN202372439 provided in the IDS submitted on February 5, 2025.
Based upon a combination of Truong et al and CN202372439, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to couple the flow cell 142 in the system taught by Truong et al to a pipe carrying the liquid medium and to use the system to measure a water concentration in a liquid medium such as a hydrocarbon/oil condensate or a natural gas liquid because the system taught by Truong et al is intended to measure a concentration of a component in a liquid medium by obtaining and analyzing infrared images of the liquid medium flowing through a passageway using a trained machine learning model, and CN202372439 teaches that a water concentration in a liquid medium comprising crude oil can be determined in a similar way by obtaining and analyzing infrared images of the crude oil flowing through a flow cell coupled to a pipe containing the crude oil.
With regards to claim 3, Truong et al teach that the trained machine learning model comprises a trained recurrent neural network. See paragraph 0093 in Truong et al where it states:
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With regards to claims 4-5, Truong et al teach that the system can further comprise a visible light camera coupled to the second side of the flow cell 142, wherein the visible light camera acquires one or more images of the liquid medium in the flow cell 142, and the at least one processor detects hydrocarbon or oil droplets in a water stream from the one or more images obtained from both the infrared camera and the visible light camera. See paragraph 0055 in Truong et al where it states:
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With regards to claim 6, CN 202372439 teaches that a flow cell 205 can be coupled to a pipe 301 using pipe flange connections 302. See Figure 2 and paragraph 0009 in the English-language translation of CN202372439 provided in the IDS submitted on February 5, 2025.
With regards to claim 7, Truong et al teach that acquiring one or more images from the infrared camera comprises acquiring a time-series of images wherein the images are separated in time by a fixed time interval. See paragraph 0087 in Truong et al where it states:
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With regards to claims 8-9, Truong et al teach of a method for measuring a concentration of a component in a liquid medium, such as the concentration of a hydrocarbon or oil in a water stream (see paragraphs 0040 and 0070-0073 in Truong et al). The method comprises acquiring one or more images from an infrared camera 146 coupled to a flow cell 142 comprising the liquid medium, detecting hydrocarbon or oil droplets in a water stream in the one or more images using a trained learning model, and determining a concentration of hydrocarbon or oil in the water stream based on the detected hydrocarbon or oil droplets. See paragraphs 0057, 0072-0073 and 0082-0083 in Truong et al provided above with regards to claims 1-2. Truong et al fail to teach that the method can be used to measure a water concentration in a liquid medium such as a hydrocarbon/oil condensate or a natural gas liquid. However, based upon a combination of Truong et al and CN202372439, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the method taught by Truong et al to measure a water concentration in a liquid medium such as a hydrocarbon/oil condensate or a natural gas liquid because the method taught by Truong et al is intended to measure a concentration of a component in a liquid medium by obtaining and analyzing infrared images of the liquid medium flowing through a passageway using a trained machine learning model, and CN202372439 teaches that a water concentration in a liquid medium comprising crude oil can be determined in a similar way by obtaining and analyzing infrared images of the crude oil flowing through a flow cell coupled to a pipe containing the crude oil.
With regards to claim 10, Truong et al teach that the trained machine learning model comprises a trained recurrent neural network. See paragraph 0093 in Truong et al provided above with regards to claim 3.
With regards to claim 11, Truong et al teach that the method can further comprise acquiring one or more images of the liquid medium in the flow cell 142 using a visible light camera, and that the at least one processor detects hydrocarbon or oil droplets in a water stream from the one or more images obtained from both the infrared camera and the visible light camera. See paragraph 0055 in Truong et al provided above with regards to claims 4-5.
With regards to claim 12, Truong et al teach that acquiring one or more images from the infrared camera comprises acquiring a time-series of images wherein the images are separated in time by a fixed time interval. See paragraph 0087 in Truong et al provided above with regards to claim 7.
With regards to claims 13-14, Truong et al teach that the method comprises training the machine learning model based on images acquired from the infrared camera 146, including synthetic training data. See paragraph 0087 in Truong et al provided above with regards to claim 7.
With regards to claims 15-16, Truong et al teach that the method further comprises determining that the concentration of the hydrocarbon or oil component in the water stream exceeds a threshold, and in response to determining that the concentration exceeds the threshold, generating an alert and commands to control fluid separation equipment to separate the hydrocarbon or oil from the water stream. See paragraph 0099 in Truong et al where it states:
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Also see paragraph 0015 in Truong et al where it states:
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With regards to claim 17, Truong et al teach of one or more non-transitory machine readable storage devices for performing the method, wherein the instructions are executable by one or more processors to cause operations comprising acquiring one or more images from an infrared camera 146 coupled to a flow cell 142 comprising a water stream, detecting hydrocarbon or oil droplets in the water stream in the one or more images using a trained learning model, and determining a concentration of hydrocarbon or oil in the water stream based on the detected hydrocarbon or oil droplets. See paragraphs 0057, 0072-0073 and 0082-0083 in Truong et al. Also, see paragraphs 0083 and 0102 in Truong et al. Truong et al fail to teach that the method performed by the instructions stored in the one or more non-transitory machine readable storage devices can be used to measure a water concentration in a liquid medium such as a hydrocarbon/oil condensate or a natural gas liquid. However, based upon a combination of Truong et al and CN202372439, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the instructions stored in the one or more non-transitory machine readable storage devices taught by Truong et al to perform a method to measure a water concentration in a liquid medium such as a hydrocarbon/oil condensate or a natural gas liquid because the method taught by Truong et al is intended to measure a concentration of a component in a liquid medium by obtaining and analyzing infrared images of the liquid medium flowing through a passageway using a trained machine learning model, and CN202372439 teaches that a water concentration in a liquid medium comprising crude oil can be determined in a similar way by obtaining and analyzing infrared images of the crude oil flowing through a flow cell coupled to a pipe containing the crude oil.
With regards to claim 18, the one or more non-transitory machine readable storage devices taught by Truong et al perform operations that further comprise acquiring one or more images of the liquid medium in the flow cell 142 using a visible light camera, and detecting hydrocarbon or oil droplets in a water stream from the one or more images obtained from both the infrared camera and the visible light camera. See paragraph 0055 in Truong et al provided above with regards to claims 4-5.
With regards to claim 19, the one or more non-transitory machine readable storage devices taught by Truong et al perform operations that comprise acquiring a time-series of images wherein the images are separated in time by a fixed time interval. See paragraph 0087 in Truong et al provided above with regards to claim 7.
With regards to claim 20, the one or more non-transitory machine readable storage devices taught by Truong et al perform operations that comprise training the machine learning model based on images acquired from the infrared camera 146. See paragraph 0087 in Truong et al provided above with regards to claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Please make note of: Nour et al (US 2026/0086015) who teach of a system and a process for monitoring liquid condensate and natural gas liquids; Parrott et al (US 2026/0079102) who teach of a spectrophotometric measurement of water in hydrocarbons; Caseres et al (US 2014/0004619) who teach of a system and a method for measuring water content and pH of a fluid mixture of water and oil in a pipeline comprising an infrared light source and a photodetector arranged on opposing sides of the pipeline; and Ahmed et al (US 2021/0396731) who teach of a measuring method of water content of petroleum fluids using a dried petroleum fluid solvent.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAUREEN M WALLENHORST whose telephone number is (571)272-1266. The examiner can normally be reached on Monday-Thursday from 6:30 AM to 4:30 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lyle Alexander, can be reached at telephone number 571-272-1254. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Examiner interviews are available via a variety of formats. See MPEP § 713.01. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/InterviewPractice.
/MAUREEN WALLENHORST/Primary Examiner, Art Unit 1797 July 29, 2026
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