DETAILED ACTION
Claim Status
Claims 1-20 are rejected.
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 .
Priority
This application does not claim foreign priority or domestic benefit. There is a child PCT application, PCT/US23/85195. Therefore, the effective filing date of claims 1-20 is 03/01/2023.
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 04/26/2023, 07/07/2023 are in
compliance with the provisions of 37 CFR 1.97 and have been considered in full. A signed copy of list of
references cited from each IDS is included with this Office Action.
Drawings
The drawings filed on 03/01/2023 are accepted.
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 1-5 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.
Claim 5 recites a step of “train the model” as dependent to a system claim. By reciting the step in terms of the action to be performed, the claim appears to be attempting to add a method step to a system claim.
Claim 1 refers to a “the cement slurry,” but this makes it unclear whether the slurry being referred to is the “final slurry design” or “a design slurry composition.” Claims 2-5 inherit this indefiniteness issue without resolving it, and are thus additionally rejected.
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 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly et al. (Proceedings of The Fourth Conference on Innovative Applications of Artificial Intelligence, 1992, henceforth “Kelly”) , and StayOnTarget’s thread on the Retrocomputing Stack Exchange (https://retrocomputing.stackexchange.com/questions/6456/why-did-expert-systems-fall, 2018).
Regarding claim 1, Kelly teaches a model called SlurryMinder (abstract) with design parameters (figure 11) and a local geographic database or “material inventory” (pg 203 ¶ 2). Kelly’s model predicts slurries that would have a thickening time within the parameters given (fig. 11, fig. 12). Kelly’s geographic database is composed of validation results of test samples, which are searched for a threshold value: “When users query their local design database, interactions between the local cement and the local chemical additives are implicitly accounted for because these previous designs have already been tested successfully in the laboratory. Queries usually consist of some design input data, such as temperature and density; the targeted physical properties of the cement; and additional items for narrowing the search, such as the well name or the client name. These criteria are used to build an SQL-like query to search a series of relational tables in the database. The output of the query consists of the average chemical additive concentration value for all tests matching the query criteria, the minimum and maximum values, and the total number of tests found.” (pg 203 ¶ 4).
SlurryMinder is an AI model (abstract), specifically an expert system (pg 207 ¶ 1). Machine learning models are also AI models.
Retrocomputing User StayOnTarget’s thread provides evidence that known work in the field of artificial intelligence has caused a design incentive to shift from expert systems to machine learning models. Retrocomputing User ConcernedOfTunbridgeWells wrote: “Like most 'next great thing' tech, expert systems found their real applications without changing the world as much as their proponents would have liked us to believe… You can see similar phenomena with big data or machine learning today. This means that any product in a trending industry will get hyped by any means possible in order to attract customers and investors. The hype tends to get picked up and echoed by lots of folks who don't really understand the technology.” (Answer 2)
Kelly is silent as to the model controlling a pumping operation within a cementing system with a pump unit, well-bore, and processor with non-transitory memory.
Kelly provides an example embodiment where a client uses SlurryMinder to identify a proper cement mixture for a client drilling an offshore oil well (pg 212 ¶ 1). Pump units and well bores are discussed in Kelly (see fig. 1 and pg 194 ¶ 1). In this embodiment, a worker would have to manually configure the cement slurry for the cementing system to work. Adding a generic computer to the cementing system to control the pumping based on the slurry design would be an obvious automation of a manual activity with the same result, similar to In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Regarding claims 2 and 3, the tested samples are generated from the design slurry and analyzed in a laboratory separate from the well site (pg 206 ¶ 3).
Regarding claim 4, communicative connection between the computer and the database is taught by Kelly (pg 199 ¶ 8).
Regarding claim 5, Kelly writes: “all laboratory personnel use this tool to perform their daily slurry designs. A specially prepared database containing over 300 tests from all parts of the region was prepared and distributed to all European locations for use with SLURRYMINDER. This region is currently delivering cement slurry designs to their customers that incorporate a uniform design philosophy and a common historical support base throughout the entire region, which includes the North Sea and the former Soviet Bloc countries.” Given that the local databases are the company’s test results at its local laboratories, it is implicit that these results are being added to the local database.
Claim 6 is identical to claim 1, only differing in that it is directed to a method rather than a system, and that there are a plurality of datasets within the geographic database. Kelly states that there are multiple local datasets within SlurryMinder (pg 203 ¶ 5).
Regarding claim 7, the geographical database is connected to the rest of the model and has tests of slurry designs (pg 203 ¶ 4).
Regarding claim 8, the water supply type and aspects of the wellbore environment are part of the parameters, such as the depth and temperature of the wellbore (fig. 11).
Regarding claim 9, the user is alerted to “a comparison value” exceeding the threshold or parameters by the search results of the SlurryMinder (fig. 12 and description).
Regarding claim 10, local cement is tested and is part of the database of Kelly (pg 203 ¶ 5).
Claim 11 is identical to claim 1, only differing in that it is directed to a method rather than a system. The arguments against claim 1 apply, mutatis mutandis.
Regarding claim 12, the design parameters of Kelly include thickening time (fig. 11).
Regarding claims 13 and 14, local cement is tested and is part of the database of Kelly (pg 203 ¶ 5).
Regarding claim 15, latex is an additive in the material inventory that provides fluid loss control (fig. 14).
Regarding claims 16 and 17, the best-ranking slurries are tested and the real results are compared to the predicted results (pg 204 ¶ 2).
Regarding claim 18, Kelly writes: “all laboratory personnel use this tool to perform their daily slurry designs. A specially prepared database containing over 300 tests from all parts of the region was prepared and distributed to all European locations for use with SLURRYMINDER. This region is currently delivering cement slurry designs to their customers that incorporate a uniform design philosophy and a common historical support base throughout the entire region, which includes the North Sea and the former Soviet Bloc countries.” Given that the local databases are the company’s test results at its local laboratories, it is implicit that these results are being added to the database.
Under Rationale F of MPEP 2143, known work in one field, such as artificial intelligence, can prompt variations for use in a different field, such as oil drilling, based on market forces if the variations are predictable, which can make an invention prima facie obvious. StayOnTarget’s Retrocomputing thread provides evidence that market forces have shifted from promoting expert systems to promoting machine learning solutions. A person of ordinary skill in the art could have re-implemented SlurryMinder as a machine learning model, and this variation would have been predictable to a person of ordinary skill in the art. Therefore, the invention is prima facie obvious.
Claim(s) 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly as evidenced by StayOnTarget as applied to claims 1-4, 6-7, 10-18 above, and further in view of Energistics (https://docs.energistics.org/WITSML/WITSML_TOPICS/WITSML-000-140-0-C-sv2000.html, 2016).
Kelly teaches the preceding claims these claims are dependent upon. Kelly is silent as to a pumping schedule.
Regarding claims 19-20, Energistics teaches a pump schedule variable that stores pump rates and fluid ratios within a cement design program (¶ 1).
Kelly teaches a slurry design (abstract).
Actively pumping the cement slurry at a wellsite with a wellbore is taught by Kelly (pg 194 fig. 1, ¶ 1).
The “unit controller” is an obvious automation of the design and pumping process, see the arguments against claim 1 and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Regarding claims 19-20, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use a pumping schedule in the text of Energistics, to automatically control pumping (¶ 1). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as they are both directed to well cementing. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Kelly by adding a pumping schedule, in order to automatically pump the cement.
Conclusion
35 U.S.C. § 101 was considered in the examination of this case, but it was found that the judicial exception was integrated into a practical application at step 2A. The pumping unit is a particular machine that integrates the exception of the optimal slurry model into the practical application of oil well cementing.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACELYN M HILL whose telephone number is (571)272-9871. The examiner can normally be reached Monday-Friday 8:30-5pm.
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/G.M.H./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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