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 .
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.
Claim(s) 1-2, 8-10 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Indo et al. (US 2014/0360257) in view of Chen et al. (US 2020/0348444).
In regard to claim 1, Indo et al. disclose a fluid sampling system, comprising: a fluid sampling tool (50) comprising: a probe (58/64) configured to draw a fluid from a formation within which the fluid sampling tool is disposed during an oil and gas well operation (paragraph 29); and a spectrometer (72) configured to detect raw data relating to optical properties of the fluid, wherein the raw data relating to the optical properties of the fluid comprises optical absorbance at a plurality of wavelengths (paragraphs 36-37); and a controller (90) comprising at least one processor (100) configured to execute coded instructions stored in memory of the controller, wherein the coded instructions, when executed by the at least one processor, cause the processor to: receive the raw data relating to the optical properties of the fluid from the spectrometer (paragraph 39). Indo et al. do not disclose that the processor is configured to preprocess the raw data relating to the optical properties of the fluid; and use a density model to predict a mass density of the fluid based on the preprocessed data relating to the optical properties of the fluid.
Chen et al. disclose a fluid sampling system wherein a processor is configured to preprocess raw data relating to optical properties of a fluid (paragraph 33, as “normalized”, also see paragraphs 34, 36, 41-42 etc.), and use a density model to predict a mass density of the fluid based on the preprocessed data relating to optical properties of the fluid (see abstract, paragraphs 46, 50 at least). It would have been obvious to one of ordinary skill in the art before the time of effective filing to provide the system of Indo et al. with the configuration as taught by Chen et al. in order to determine contamination of the fluid sample (paragraph 44) since combining prior art elements according to known techniques to yield predictable results is considered obvious to one of ordinary skill.
In regard to claim 2, Chen et al. disclose wherein preprocessing the raw data relating to the optical properties of the fluid comprises normalizing the raw data relating to the optical properties of the fluid to one or more hydrocarbon frequency channels (paragraphs 33-34, as normalized to its respective detector/channel).
In regard to claim 8, Chen et al. disclose wherein the density model is trained with training data including optical absorption data, pressure data, temperature data and density data (paragraph 26 as calibrated on optical-PVT database, where as best understood this includes optical, pressure, temperature and density data).
In regard to claim 9, Indo et al. disclose wherein the fluid sampling tool comprises the controller (paragraph 39).
In regard to claim 10 Indo et al. disclose a method comprising. disposing a fluid sampling tool (50) within a wellbore; drawing a fluid from a formation within the wellbore, within which the fluid sampling tool is disposed, using a probe (58/64); detecting raw data relating to optical properties of the fluid using a spectrometer (72), wherein the raw data relating to the optical properties of the fluid comprises optical absorbance at a plurality of wavelengths (paragraphs 36-37); receiving, by a controller, the raw data relating to the optical properties of the fluid from the spectrometer (paragraph 39); and wherein the controller comprises at least one processor (100) configured to execute coded instructions stored in a memory of the controller. Indo et al. do not disclose preprocessing, by the controller, the raw data relating to the optical properties of the fluid; and using a density model, by the controller, to predict a mass density of the fluid based on the preprocessed data relating to the optical properties of the fluid.
Chen et al. disclose a method including preprocessing raw data relating to optical properties of a fluid (paragraph 33, as “normalized”, also see paragraphs 34, 36, 41-42 etc.), and using a density model to predict a mass density of the fluid based on the preprocessed data relating to optical properties of the fluid (see abstract, paragraphs 46, 50 at least). It would have been obvious to one of ordinary skill in the art before the time of effective filing to provide the method of Indo et al. with the configuration as taught by Chen et al. in order to determine contamination of the fluid sample (paragraph 44) since combining prior art elements according to known techniques to yield predictable results is considered obvious to one of ordinary skill.
In regard to claim 11, Chen et al. disclose wherein preprocessing the raw data relating to the optical properties of the fluid comprises normalizing the raw data relating to the optical properties of the fluid to one or more hydrocarbon frequency channels (paragraphs 33-34, as normalized to its respective detector/channel).
In regard to claim 17, Chen et al. disclose wherein the density model is trained with training data including optical absorption data, pressure data, temperature data and density data (paragraph 26 as calibrated on optical-PVT database, where as best understood this includes optical, pressure, temperature and density data).
In regard to claim 18, Indo et al. disclose wherein the fluid sampling tool comprises the controller (paragraph 39).
Claim(s) 3 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Indo et al. in view of Chen et al. ‘444 as applied to claim 1 and 10 above, and further in view of Chen et al. (US 2016/0320527).
In regard to claim 3, Indo et al. and Chen et al. ‘444 disclose all the limitations of this claim, as applied to claim 1 above, except for wherein the coded instructions, when executed by the at least one processor, cause the processor to: use a composition model to predict compositions of one or more components of the fluid; and use the density model to predict the mass density of the fluid based at least in part on the predicted compositions of the one or more components of the fluid.
Chen et al. ‘527 disclose a fluid sampling system wherein a processor includes coded instructions, when executed by the at least one processor, cause the processor to: use a composition model to predict compositions of one or more components of the fluid (paragraph 24, “fluid characterization model”); and use the density model to predict the mass density of the fluid based at least in part on the predicted compositions of the one or more components of the fluid (paragraph 24, where the same model provides density, as being “based on”). It would have been obvious to one of ordinary skill at the time of effective filing to provide the system of Indo et al. as modified by Chen et al. ‘444 with the fluid model capabilities as taught by Chen et al. ‘527 in order to provide more accurate density predictions by incorporating compositional information.
In regard to claim 12, Indo et al. and Chen et al. ‘444 disclose all the limitations of this claim, as applied to claim 10 above, except for using a composition model to predict compositions of one or more components of the fluid; and using the density model to predict the mass density of the fluid based at least in part on the predicted compositions of the one or more components of the fluid.
Chen et al. ‘527 disclose a method including using a composition model to predict compositions of one or more components of the fluid (paragraph 24, “fluid characterization model”); and using the density model to predict the mass density of the fluid based at least in part on the predicted compositions of the one or more components of the fluid (paragraph 24, where the same model provides density, as being “based on”). It would have been obvious to one of ordinary skill at the time of effective filing to provide the method of Indo et al. as modified by Chen et al. ‘444 with the fluid model capabilities as taught by Chen et al. ‘527 in order to provide more accurate density predictions by incorporating compositional information.
Claim(s) 4-7 and 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Indo et al. in view of Chen et al. ‘444 and Chen et al. ‘527 as applied to claims 3 and 12 above, and further in view of Wu et al. (US 2014/0249876).
In regard to claims 4-7, Indo et al., Chen et al. ‘444 and Chen et al. ‘527 disclose all the limitations of these claims, except for specifying a type of model (i.e. physics-based, statistical, machine learning, or artificial neural network). Wu et al. disclose a system wherein a model for predictions can be physics based, statistical, machine learning or artificial neural network based (paragraph 38). It would have been obvious to one of ordinary skill in the art before the time of effective filing to provide either of the models in the system as taught by Indo et al., as modified by Chen et al. ‘444 and ‘527, to comprise at least one of these types of models since choosing among known options (for a model) with a reasonable expectation of success is considered obvious to one of ordinary skill.
In regard to claims 13-16, Indo et al., Chen et al. ‘444 and Chen et al. ‘527 disclose all the limitations of these claims, except for specifying a type of model (i.e. physics-based, statistical, machine learning, or artificial neural network). Wu et al. disclose a method wherein a model for predictions can be physics based, statistical, machine learning or artificial neural network based (paragraph 38). It would have been obvious to one of ordinary skill in the art before the time of effective filing to provide either of the models in the method as taught by Indo et al., as modified by Chen et al. ‘444 and ‘527, to comprise at least one of these types of models since choosing among known options (for a model) with a reasonable expectation of success is considered obvious to one of ordinary skill.
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 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 3 and 10 of U.S. Patent No. 12,631,553. Although the claims at issue are not identical, they are not patentably distinct from each other because because instant claim 1 is generic to all that is recited in claim 3 of U.S. Patent No. 12,631,553. In other words, claim 3 of U.S. Patent No. 12,631,553 fully encompasses the subject matter of claim 1 and therefore anticipates claim 1. Since claim 1 is anticipated by claim 3 of the patent, it is not patentably distinct from claim 3. Thus the invention of claim 3 of the patent is in effect a “species” of the “generic” invention of claim 1. It has been held that the generic invention is anticipated by the species, see In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since claim 1 is anticipated (fully encompassed) by claim 3 of the patent, claim 1 is not patentably distinct from claim 3, regardless of any additional subject matter present in claim 3. Claims 2-18 also appear similarly encompassed by the claims of ‘553.
Conclusion
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/D. ANDREWS/Primary Examiner, Art Unit 3672
9/28/2026
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