DETAILED ACTION
Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are currently pending in this application.
Priority
2. No foreign priority has been claimed. This application is claims benefit to provisional application 63/187,734.
Specification
3. The disclosure is objected to because of the following informalities: a cross reference to related applications section noting the provisional application is missing.
Appropriate correction is required.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 3/11/2022 was received. The submission is in compliance with the provisions of 37 CFR 1.97 and 37 CFR 1.98. Accordingly, the information disclosure statement has being considered by the examiner.
Drawings
5. The drawings submitted on 3/11/2022 are in compliance with 37 CFR § 1.81 and 37 CFR § 1.83 and have been accepted by the examiner.
Claim Rejections - 35 USC § 101 Non-Statutory
6. 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.
7. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Specifically, representative Claim 1 recites:
A method for identifying a material behind a pipe string comprising:
disposing an acoustic logging tool into a wellbore;
insonifying a pipe string within the wellbore with the acoustic logging tool;
recording sonic or ultrasonic data with the acoustic logging tool;
inputting the sonic or ultrasonic data into a trained machine learning model; and
identifying the material behind the pipe string using the trained machine learning model.
or (ii) if the machine learning model cannot identify the material behind the pipe string, operating the machine learning model to classify the state of the material behind the pipe string by comparing the sonic or ultrasonic data to a library of data samples that have been previously interpreted and classified.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Similar limitations comprise the abstract ideas of Claims 10 and 14.
Under Step 1 of the analysis, claim 1 does belong to a statutory category, namely it is a process claim. Likewise, claim 10 is a process claim and claim 14 a system claim.
Under Step 2A, prong 1, claim 1 is found to include at least one judicial exception, that being a mental process. This can be seen in the claim limitations of disposing an acoustic logging tool into a wellbore; inputting the sonic or ultrasonic data into a trained machine learning model; and identifying the material behind the pipe string using the trained machine learning model or (ii) if the machine learning model cannot identify the material behind the pipe string, operating the machine learning model to classify the state of the material behind the pipe string by comparing the sonic or ultrasonic data to a library of data samples that have been previously interpreted and classified which is the judicial exception of a mental process because it is merely a data evaluation including calculations, and/or judgements capable of being performed mentally.
Similar limitations comprise the abstract ideas of Claims 10 and 14.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in claim 1, the claimed method recites additional elements including insonifying a pipe string within the wellbore with the acoustic logging tool; and recording sonic or ultrasonic data with the acoustic logging tool. (claims 1, 10, and 14) which are merely data gathering steps recited at a high level of generality and therefore merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”. The claim also recites “a trained machine learning model” (claims 1, 10, and 14) however the “machine learning” is recited at a high level of generality, e.g. Spec. [0024] describing a variety of different types of “machine learning” models that may be used, and merely amounts to the use of computer technology as a tool to apply the abstract idea (see MPEP 2106.05(f)) and/or the use of “machine learning” to perform the predictions, that are otherwise abstract, is merely an attempt at limiting the abstract to a particular field of use (See MPEP 2106.05(h)).
The generic data gathering, processing, and output steps, and other elements, are recited so generically (no details whatsoever are provided other than e.g., “disposing” and “recording”) that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed system. For instance, nothing is done with the data identified from the learning model.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely insignificant extra-solution activity (claims 1, 10, and 14). Specifically, the data is classified or stored. Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1, as well as claims 10 and 14, amount to significantly more than the abstract idea.
With regards to the dependent claims, claims 2-9, 11-13, and 15-20, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements therefore these claims are found ineligible for the reasons described for independent claims 1, 8, and 15.
See Supreme court decision in Alice Corporation Pty. Ltd. V. CLS Bank International, et al.
Claim Rejections - 35 USC § 102
8. 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 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.
9. 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.
10. Claims 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kalyanraman et al. US 12,091,960 .
With regards to Claim 1, Kalyanraman et al. US 12,091,960 teaches a method for identifying a material behind a pipe string comprising:
disposing an acoustic logging tool into a wellbore;(216; figure 2A)
insonifying a pipe string within the wellbore with the acoustic logging tool; (256,258,260; figure 2B) (Col. 7, lines 60-67)
recording sonic or ultrasonic data with the acoustic logging tool; (232; figure 2A)
inputting the sonic or ultrasonic data into a trained machine learning model; (Col. 7, lines 43-57) and
identifying the material behind the pipe string using the trained machine learning model. (material composition; Col. 31, lines 45-54)
or
(ii) if the machine learning model cannot identify the material behind the pipe string, operating the machine learning model to classify the state of the material behind the pipe string by comparing the sonic or ultrasonic data to a library of data samples that have been previously interpreted and classified. (Col. 18, lines 1-8, Col. 26, lines 23-32, Col. 27, lines 33-40, Col. 29, lines 42-61)
With regards to Claim 2, Kalyanraman et al. US 12,091,960 teaches identifying flexural wave mode data from the sonic or ultrasonic data. (Col. 36, lines 27-32)
With regards to claim 3, Kalyanraman et al. US 12,091,960 teaches calculating an acoustic impedance, an eccentricity, and a thickness of the pipe string from the sonic or ultrasonic data. (Col.6, lines 8-17)
With regards to claim 4, Kalyanraman et al. US 12,091,960 teaches calculating one or more flexural wave attributes from one or more receivers using the sonic or ultrasonic data. (Col. 5, lines 49-51)
With regards to claim 5, Kalyanraman et al. US 12,091,960 teaches identifying the material behind the pipe string at one or more depths and one or more azimuths in the wellbore. (Col. 22, lines 41-45)
With regards to claim 6, Kalyanraman et al. US 12,091,960 teaches an input comprising at least an acoustic impedance, an eccentricity, a pipe string thickness, or one or more acoustic wave attributes is used to train the trained machine learning model. (Col. 6, lines 11-15)
With regards to claim 7, Kalyanraman et al. US 12,091,960 teaches determining a correlation between the input and a matched output of the trained machine learning model. (Col. 34, lines 6-21)3
With regards to claim 8, Kalyanraman et al. US 12,091,960 teaches the sonic or ultrasonic data is from a pulsed-echo operation, a pitch-catch operation, or any combination thereof. (Col. 7, lines 35-42)
With regards to claim 9, Kalyanraman et al. US 12,091,960 teaches the input is synthetic data, lab data, or one or more actual measurements. (Col. 7, lines 1-6)
With regards to claim 10, Kalyanraman et al. US 12,091,960 teaches a method for identifying a material behind a pipe string comprising:
generating synthetic modeled data from a plurality of lab data and one or more models;(Col. 36, lines 5-9)
training a machine learning model with the synthetic modeled data; (Col. 35-36, lines 65-67 ,1-4, &17-20) and
identifying an accuracy of the machine learning model. (Col. 36, lines 17-20)
such that if the machine learning model is unable to identify the material behind the pipe string based on specific recorded sonic or ultrasonic data, the machine learning model classifies the state of the material behind the pipe string by comparing the sonic or ultrasonic data to a library of data samples that have been previously interpreted and classified. (Col. 18, lines 1-8, Col. 26, lines 23-32, Col. 27, lines 33-40, Col. 29, lines 42-61)
With regards to claim 11, Kalyanraman et al. US 12,091,960 teaches the one or more models comprises a pulse-echo synthetic model or a pitch-catch synthetic model. (col. 7, lines 39-42)
With regards to claim 12, Kalyanraman et al. US 12,091,960 teaches the machine learning model comprises one or more variables that comprise one or more pipe casing thickness, an eccentricity of an acoustic logging tool, and a mud in the pipe string. ( Col. 6, lines 10-14)
With regards to claim 13, Kalyanraman et al. US 12,091,960 teaches training the machine learning model by determining a correlation between the one or more variables and a matched output. (Col. 29, lines 29-32) (Col. 36, lines 4-7)
With regards to claim 14, Kalyanraman et al. US 12,091,960 teaches a system for identifying a material behind a pipe string comprising:
an acoustic logging tool(216; figure 2A) comprising:
one or more transmitters for insonifying a pipe string within a wellbore; (256,258,260; figure 2B) (Col. 7, lines 60-67)
one or more receivers for recording sonic or ultrasonic data; (232; figure 2A)and
a transducer configured to record a sonic or ultrasonic data; (Col. 5, lines 49-51)and
an information handling system that: inputs the sonic or ultrasonic data into a machine learning model; (Col. 7, lines 43-57) and
identifies the material behind the pipe string using the machine learning model. (material composition; Col. 31, lines 45-54)
or (ii) classifies the state of the material behind the pipe string by comparing the sonic or ultrasonic data to a library of data samples that have been previously interpreted and classified. (Col. 18, lines 1-8, Col. 26, lines 23-32, Col. 27, lines 33-40, Col. 29, lines 42-61)
With regards to claim 15, Kalyanraman et al. US 12,091,960 teaches the information handling system further identifies flexural wave mode data from the sonic or ultrasonic data recorded by the one or more receivers or the transducer. (Col. 36, lines 27-32)
With regards to claim 16, Kalyanraman et al. US 12,091,960 teaches the information handling system further identifies an acoustic impedance, an eccentricity, and a thickness of the pipe string from the sonic or ultrasonic data recorded by the one or more receivers or the transducer. (Col. 6, lines 8-17)
With regards to claim 17, Kalyanraman et al. US 12,091,960 teaches the information handling system identifies one or more flexural wave attributes from the one or more receivers using the sonic or ultrasonic data recorded by the one or more receivers or the transducer. (Col. 5, lines 49-51)
With regards to claim 18, Kalyanraman et al. US 12,091,960 teaches the information handling system identifies the material behind the pipe string at one or more depths and one or more azimuths in the wellbore. (Col. 22, lines 41-45)
With regards to claim 19, Kalyanraman et al. US 12,091,960 teaches the information handling system trains the machine learning model with a pattern recognition. (Col. 29, lines 29-32) Comparing to other wells would require recognizing similarities or patterns
With regards to claim 20, Kalyanraman et al. US 12,091,960 teaches one or more acoustic impedance images are used for the pattern recognition. (Col. 7, lines 20-26)
Response to Arguments
11. Applicant's arguments filed 8/12/2025 have been fully considered but they are not persuasive.
12. Applicant is reminded that during patent examination, the pending claims must be "given the broadest reasonable interpretation consistent with the specification." Applicant always has the opportunity to amend the claims during prosecution, and broad interpretation by the examiner reduces the possibility that the claim, once issued, will be interpreted more broadly than is justified. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-51 (CCPA 1969).
13. While the meaning of claims of issued patents are interpreted in light of the specification, prosecution history, prior art and other claims, this is not the mode of claim interpretation to be applied during examination. During examination, the claims must be interpreted as broadly as their terms reasonably allowed. This means that the words of the claim must be given their plain meaning unless applicant has provided a clear definition in the specification. In re Zletz, 893 F.2d 319, 321, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989).
14. In this instance applicant argues that the prior art of record does not teach if the machine learning model cannot identify the material behind the pipe string, operating the machine learning model to classify the state of the material behind the pipe string by comparing the sonic or ultrasonic data to a library of data samples that have been previously interpreted and classified. The cited sections (Col. 18, lines 1-8, Col. 26, lines 23-32, Col. 27, lines 33-60, Col. 29, lines 42-61) above teaches using AI to interpret data using a library and classify it by updating a library. This appears to read on the new claim limitation. It should also be noted that the limitation is not required in some of the claims as the word “or” .
15. With regards to the USC 101, applicant states that the claims do not recite a judicial exception. The claims are directed to a mental process because it is merely a data evaluation including calculations, and/or judgements capable of being performed mentally. While the claims do contain tangible elements that are used in data collection, the analysis of the data is considered the mental process. For example, recording, inputting, identifying, etc. are tasks that maybe completed by a human i.e. Recording maybe writing data on a piece of paper or inputting data into a computer. Applicant goes on to argue that acoustic logging tool is an additional element that integrates the claim into a practical application. However, this would be considered a data gathering apparatus and would not be considered to incorporate the abstract idea into a practical application. Applicant argues that the acoustic logging tool would reflect an improvement in the functioning of a computer or an improvement to other technology or technical field. As previously stated, this is considered to be a data gathering tool as it is unclear how the function of a computer or other field would be improved. Applicant also argues that the Finjan memo found that specific steps that accomplish a result that realize an improvement are considered patent eligible. Applicant goes on to recite the entire claim as specific steps, therefore it is unclear which particular step applicant considers as an improvement. Applicant also argues that step 2B is unnecessary and improper because the claim should be eligible at step 2A. Examiner disagrees as shown in the analysis above as the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Conclusion
16. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
17. A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
18. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADITYA S BHAT whose telephone number is (571)272-2270. The examiner can normally be reached on Monday-Friday 8 am-6pm.
19. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
20. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby Turner can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
21. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ADITYA S BHAT/Primary Examiner, Art Unit 2857 December 23, 2025