DETAILED ACTION
This is in response to the application filed on July 11, 2023 where Claims 1 – 20, of which Claims 1, 11, and 20 are in independent form, are presented for examination.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 17, 2024 was filed before the mailing date of the current action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
1. Regarding Claims 11 – 19, the claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed system is not a process, machine, manufacture, or composition of matter. Given the broadest reasonable interpretation, the terms “storage” and “processor” are not specifically defined to describe tangible, hardware configuration to make the claimed system into a machine. The specification describes storage as a database [Fig. 5; Para. 0051-53] and storage medium to potentially be transitory mediums [Para. 0093 – phrases “may be” and “but not limited to” do not preclude other types of mediums; Para. 0097 – expressly includes carrier waves]. The use of virtualized technologies is also not precluded based on the specification, such as virtualized processors or network interfaces. Therefore, the claims are rejected under 101.
2. Regarding Claim 20, the claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed well drilling is not a process, machine, manufacture, or composition of matter. The claimed “well drilling,” “storage medium,” “network interface,” and “processor” are not specifically defined to describe tangible, hardware configuration to make the claimed well drilling into a machine. Given the broadest reasonable interpretation, the terms “storage” and “processor” are not specifically defined to describe tangible, hardware configuration to make the claimed system into a machine. The specification describes a storage medium to potentially be transitory mediums [Para. 0093 – phrases “may be” and “but not limited to” do not preclude other types of mediums; Para. 0097 – expressly includes carrier waves]. The use of virtualized technologies is also not precluded based on the specification, such as virtualized processors or network interfaces. Therefore, the claim is rejected under 101.
3. Regarding Claims 1 – 10, the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the steps of “receiving a reliability report,” “extracting name information,” “determining a full name of the facility,” and “identify[ing] operating conditions for equipment at the facility” which are steps or processes that can be performed in the human mind by a per. The steps are considered an abstract idea.
This judicial exception is not integrated into a practical application because the additional limitations merely describe the type of information being extracted or being compared to in a reliability report and the general use of “training a ML model” to performing the identifying of operating conditions for equipment at the facility based on the reliability report. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the “training a ML model” merely adds the words “apply it” to identifying operating conditions via a ML model. No additional limitations within the claims provide particular improvements or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea with a ML model. See 2024 Subject Matter Eligibility Update, Example 47, Claim 2, Pgs. 25-32. Therefore, the claims are rejected under 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by PGPub. 2022/0090485 (hereinafter “Boguslawski”).
4. Regarding Claim 20, Boguslawski discloses of a well drilling [Fig. 1; Abstract; Para. 0041-42] comprising:
a storage medium comprising a machine learning model configured to infer operating conditions from a plurality of reliability reports, wherein the reliability reports are generated based on a maintenance associated with at least one equipment [Fig. 2; Para. 0045, 0051];
a network interface [Fig. 2; Para. 0045]; and
a processor configured to execute instructions and cause the processor [Fig. 2; Para. 0045-47] to:
receive measurements from equipment located at a facility [Fig. 9; Para. 0051, 0067-71];
identify at least one notification pertaining to operating conditions of the equipment based on the machine learning model and a location of the facility [Figs. 14A-15; Para. 0051, 0067-71, 0078-80]; and
send the at least one notification to a client device [Fig. 15; Para. 0089-90].
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 – 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boguslawski., in view of PGPub. 2021/0230981 (hereinafter “Gupta”).
5. Regarding Claims 1 and 11, Boguskawski discloses a system for calibrating reliability records [Figs. 1 and 2; Abstract; Para. 0041-42; Claim 1], comprising:
a storage configured to store instructions [Fig. 2; Para. 0045-47; Claim 1];
a processor configured to execute the instructions and cause the processor [Fig. 2; Para. 0045-47; Claim 1] to:
receive a reliability report and extract information related to operation of equipment at a facility [Fig. 9; Para. 0069-71, 0078-80];
train a machine learning model to identify adverse conditions for equipment disposed in a well environment based on the reliability report [Figs. 14A-15; Para. 0051, 0067-71, 0078-80],
Boguskawski, however, does not specifically disclose of extracting name information associated with the facility from the reliability report, determining a full name of the facility based on the name information; and training a machine learning model to identify adverse conditions for equipment based on the unique identifier and the full name of the facility.
Gupta discloses a system and method of processing extracting words associated with the oilfield from unstructured documents and determining the words after cleansing and stemming (extracting name information associated with the facility from the reliability report; determining a full name of the facility based on the name information) [Fig. 10; Para. 0048, 0101]. Gupta also discloses of training a ML model to generate meaningful insights based on the unique identifier and the words of the oilfield (training a machine learning model to identify adverse conditions for equipment based on the unique identifier and the full name of the facility) [Fig. 10; Para. 0045, 0066, 0101-104]. It would have been obvious to one skilled in the art before the effective filing date of the current invention to incorporate the teachings of Gupta with Boguskawski since both systems train ML models to detect conditions for an oilfield. The combination would enable the Boguskawski system to utilize the name of the oilfield from all training data to process and aggregate various conditions and data for a particular oilfield. The motivation to do so is to accurately associate data to specific identifiers and entities (obvious to one skilled in the art).
6. Regarding Claims 2 and 12, Boguskawski, in view of Gupta, discloses the limitations of Claims 1 and 11. Gupta further discloses that the unique identifier includes the name information, and wherein the name information matches at least one other unique identifier [Para. 0045, 0066, 0101-104].
7. Regarding Claims 3 and 13, Boguskawski, in view of Gupta, discloses the limitations of Claims 1 and 11. Gupta further discloses that extracting the information related to the operation of the equipment at the facility [Fig. 10; Para. 0045, 0066, 0101-104]:
extracting n-grams from a free-form text field associated with the reliability report; classifying the operation of the facility based on labels applied to the n-grams into at least one classification [Para. 0052, 0099, 0101]; and
determining at least one source associated with an adverse operating condition of an equipment at the facility based on the at least one classification [Para. 0052, 0099, 0101].
8. Regarding Claims 4 and 14, Boguskawski, in view of Gupta, discloses the limitations of Claims 3 and 13. The combination of Boguskawski and Gupta further discloses of labeling the n-grams with a plurality of labels based on a second machine learning model [Gupta; Para. 0099, 0101], wherein the labels include at least one of properties associated with the equipment at the facility, physical properties of materials, or geographical properties associated with facility [Boguskawski; Para. 0003-4].
9. Regarding Claims 5 and 15, Boguskawski, in view of Gupta, discloses the limitations of Claims 4 and 14. Boguskawski further discloses that the facility comprises a well pumping system configured to extract materials from a downhole of a well [Para. 0037-39, 0067].
10. Regarding Claims 6 and 16, Boguskawski, in view of Gupta, discloses the limitations of Claims 4 and 14. Boguskawski further discloses that the machine learning model is configured to infer operations of a well drilling system at the facility, and wherein the well drilling system is installed into a well at the facility [Para. 0041, 0072].
11. Regarding Claims 7 and 17, Boguskawski, in view of Gupta, discloses the limitations of Claims 3 and 13. The combination of Boguskawski and Gupta further discloses of identifying a response corresponding to the at least one source associated with the adverse operating condition based on the n-grams [Boguskawski; Para. 0037-39, 0067; Claims 11-13; Gupta; Para. 0101].
12. Regarding Claims 8 and 18, Boguskawski, in view of Gupta, discloses the limitations of Claims 7 and 17. Boguskawski further discloses of identifying an equipment associated with at least one of the response or the root cause based on the unique identifier of the facility [Para. 0037-39, 0067; Claim 1].
13. Regarding Claims 9 and 19, Boguskawski, in view of Gupta, discloses the limitations of Claims 1 and 11. Boguskawski further discloses that the machine learning model is configured to receive runtime information, identify at least one condition based on the runtime information, and output a notification related to the at least one condition [Para. 0036-37]
14. Regarding Claim 10, Boguskawski, in view of Gupta, discloses the limitations of Claim 1. The combination of Boguskawski and Gupta further discloses that the full name comprises a unique identifier, information pertaining to equipment at the facility is stored in a data source and includes the unique identifier, reliability reports and unstructured information associated with the facility and stored in the data source include the unique identifier, and time series data associated with measurements from the equipment at the facility stored in the data source include the unique identifier [Boguskawski, Fig. 9; Para. 0069-71, 0078-80; Claims 11-12; Gupta, Fig. 10; Para. 0048, 0101; Claim 3].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. PGPub. 2017/0293842; PGPub. 2023/0366303.
Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tae K. Kim, whose telephone number is (571) 270-1979. The examiner can normally be reached on Monday - Friday (10:00 AM - 6:30 PM EST).
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/TAE K KIM/Primary Examiner, Art Unit 2496