Detailed Action
Double Patenting Rejections
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.
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Claim(s) 1-18 rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 1-17 of U.S. Patent No.12,416,233. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the patent recite a method of using a trained machine learning model to classify mud pulse signals, whereas the claims of the instant application are broader version of claim(s) of the patent as illustrated below. Therefore, the claims of the instant application are encompassed by the claims of the patent, and it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to arrive to the broader claims as recited in the instant application.
Instant Application
US Patent
12,416,233
1. A method for using a trained machine learning model to classify mud pulse signals, the method comprising:
receiving a mud pulse signal from a measurement while drilling (MWD) tool, wherein the mud pulse signal comprises data;
decoding, using the trained machine learning model, the data to determine a value of the data; and
providing a user interface comprising the value of the data for presentation on a computing device of a user,
wherein the user interface comprises a graphical element that enables repositioning at least a portion of the mud pulse signal into a slot that causes the value of the data to be modified.
1. A method for using a trained machine learning model to classify mud pulse signals, the method comprising:
receiving a mud pulse signal from a measurement while drilling (MWD) tool, wherein the mud pulse signal comprises data;
decoding, using the trained machine learning model, the data to determine a value of the data;
providing a user interface comprising the value of the data for presentation on a computing device of a user;
providing a second user interface for presentation on the computing device of the user, wherein the second user interface presents a graphical element for identifying where a synchronization signal is located in the mud pulse signal;
receiving, from the computing device, a message identifying where the synchronization signal is located in the mud pulse signal; and
retraining, using the message, the trained machine learning model to classify the data in the mud pulse signal by identifying the synchronization signal, such that the trained machine learning model identifies subsequent synchronization signals in subsequent mud pulse signals based on where the synchronization signal is located in the mud pulse signal.
8. The method of claim 1, wherein the user interface comprises a graphical element that enables repositioning at least a portion of the mud pulse signal into a slot that causes the value of the data to be modified.
2. The method of claim 1, wherein the user interface presents the value of the data in a first graphical element and concurrently presents a second graphical element capable of enabling confirmation of whether the value is valid or invalid.
2. The method of claim 1, wherein the user interface presents the value of the data in a first graphical element and concurrently presents a second graphical element capable of enabling confirmation of whether the value is valid or invalid.
3. The method of claim 2, further comprising:
receiving, from the computing device, a message classifying the value as valid or invalid, wherein the message is transmitted from the computing device in response to the second graphical element being utilized; and
updating, based on the message, the trained machine learning model to classify the data in the mud pulse signal as representing the value, such that the trained machine learning model decodes, based on the message, a subsequent mud pulse signal that is similar to the mud pulse signal.
3. The method of claim 2, further comprising:
receiving, from the computing device, a message classifying the value as valid or invalid, wherein the message is transmitted from the computing device in response to the second graphical element being utilized; and
updating, based on the message, the trained machine learning model to classify the data in the mud pulse signal as representing the value, such that the trained machine learning model decodes, based on the message, a subsequent mud pulse signal that is similar to the mud pulse signal.
4. The method of claim 1, wherein the trained machine learning model is trained using a dataset comprising inputs including a plurality of signals representing mud pulse signals and target outputs including a plurality of values representing data of the plurality of signals.
4. The method of claim 1, wherein the trained machine learning model is trained using a dataset comprising inputs including a plurality of signals representing mud pulse signals and target outputs including a plurality of values representing data of the plurality of signals.
5. The method of claim 1, further comprising;
providing a second user interface for presentation on the computing device of the user, wherein the second user interface presents a graphical element for identifying where a synchronization signal is located in the mud pulse signal;
receiving, from the computing device, a message identifying where the synchronization signal is located in the mud pulse signal; and
updating, using the message, the trained machine learning model to classify the data in the mud pulse signal by identifying the synchronization signal, such that the trained machine learning model identifies subsequent synchronization signals in subsequent mud pulse signals based on where the synchronization signal is located in the mud pulse signal.
Part of Claim 1: providing a second user interface for presentation on the computing device of the user, wherein the second user interface presents a graphical element for identifying where a synchronization signal is located in the mud pulse signal;
receiving, from the computing device, a message identifying where the synchronization signal is located in the mud pulse signal; and
retraining, using the message, the trained machine learning model to classify the data in the mud pulse signal by identifying the synchronization signal, such that the trained machine learning model identifies subsequent synchronization signals in subsequent mud pulse signals based on where the synchronization signal is located in the mud pulse signal.
6. The method of claim 1, wherein the trained machine learning model is trained to decode the data based on training data comprising a plurality of slots, wherein each slot of the plurality of slots represents a value when a respective mud pulse signal is detected in the slot.
6. The method of claim 1, wherein the trained machine learning model is trained to decode the data based on training data comprising a plurality of slots, wherein each slot of the plurality of slots represents a value when a respective mud pulse signal is detected in the slot.
7. The method of claim 1, wherein the decoding further comprises:
using a plurality of trained machine learning models to decode the data to determine a plurality of values of the data.
7. The method of claim 1, wherein the decoding further comprises:
using a plurality of trained machine learning models to decode the data to determine a plurality of values of the data; and
determining the value of the data based on one or more criteria associated with the plurality of values of the data, the one or more criteria comprising (i) a majority of the plurality of values that match, (ii) a weighted sum, (iii) a non-linear evaluation function, (iv) a low-pass filter, (v) a determination of how close the value is to a prior value, (vi) a fuzzy logic selection, or some combination thereof.
8. The method of claim 7, further comprising determining the value of the data based on one or more criteria associated with the plurality of values of the data, the one or more criteria comprising (i) a majority of the plurality of values that match, (ii) a weighted sum, (iii) a non- linear evaluation function, (iv) a low-pass filter, (v) a determination of how close the value is to a prior value, (vi) a fuzzy logic selection, or some combination thereof.
Part of claim 7: determining the value of the data based on one or more criteria associated with the plurality of values of the data, the one or more criteria comprising (i) a majority of the plurality of values that match, (ii) a weighted sum, (iii) a non-linear evaluation function, (iv) a low-pass filter, (v) a determination of how close the value is to a prior value, (vi) a fuzzy logic selection, or some combination thereof.
9. The method of claim 1, wherein the mud pulse signal is received by a pressure transducer coupled to a data acquisition system, and the mud pulse signal is transmitted by the data acquisition system to a surface processor that relays the mud pulse signal to a cloud-based computing system capable of decoding, using the trained machine learning model, the data included in the mud pulse signal.
9. The method of claim 1, wherein the mud pulse signal is received by a pressure transducer coupled to a data acquisition system, and the mud pulse signal is transmitted by the data acquisition system to a surface processor that relays the mud pulse signal to a cloud-based computing system capable of decoding, using the trained machine learning model, the data included in the mud pulse signal.
10. The method of claim 9, wherein, prior to relaying the mud pulse signal to the cloud-based computing system, the surface processor determines whether one or more characteristics of a network satisfy one or more thresholds.
10. The method of claim 9, wherein, prior to relaying the mud pulse signal to the cloud-based computing system, the surface processor determines whether one or more characteristics of a network satisfy one or more thresholds.
11. The method of claim 10, wherein, responsive to determining the one or more characteristics of the network satisfy the one or more thresholds, the surface processor transmits the mud pulse signal to the cloud-based computing system.
11. The method of claim 10, wherein, responsive to determining the one or more characteristics of the network satisfy the one or more thresholds, the surface processor transmits the mud pulse signal to the cloud-based computing system.
12. The method of claim 10, wherein, responsive to determining the one or more characteristics of the network do not satisfy the one or more thresholds, the surface processor downsamples, compresses, or both the mud pulse signal to generate a modified mud pulse signal and transmits the modified mud pulse signal to the cloud-based computing system.
12. The method of claim 10, wherein, responsive to determining the one or more characteristics of the network do not satisfy the one or more thresholds, the surface processor downsamples, compresses, or both the mud pulse signal to generate a modified mud pulse signal and transmits the modified mud pulse signal to the cloud-based computing system.
13. The method of claim 9, wherein, prior to relaying the mud pulse signal to the cloud-based computing system, the surface processor determines whether the cloud-based computing system is available.
13. The method of claim 9, wherein, prior to relaying the mud pulse signal to the cloud-based computing system, the surface processor determines whether the cloud-based computing system is available.
14. The method of claim 1, wherein decoding, using the trained machine learning model, the data to determine the value of the data comprises:
inputting the mud pulse signal into a filter container comprising a plurality of paths, wherein the plurality of paths comprise:
a first path including a filter component and a first decoder component,
a second path including a machine learning signal filter component and a second decoder component,
a third path including a machine learning decoder component,
or some combination thereof.
14. The method of claim 1, wherein decoding, using the trained machine learning model, the data to determine the value of the data comprises:
inputting the mud pulse signal into a filter container comprising a plurality of paths, wherein the plurality of paths comprise:
a first path including a filter component and a first decoder component,
a second path including a machine learning signal filter component and a second decoder component,
a third path including a machine learning decoder component,
or some combination thereof.
15. The method of claim 14, further comprising:
generating, from the plurality of paths, a plurality of values of the data; and
determining, using a multi-path voting matrix component, the value of the data based on the plurality of values, wherein the determining comprises:
identifying a plurality of confidence levels associated with the plurality of paths, and
selecting the value of the data generated by one of the plurality of paths based on its respective confidence level.
15. The method of claim 14, further comprising:
generating, from the plurality of paths, a plurality of values of the data; and
determining, using a multi-path voting matrix component, the value of the data based on the plurality of values, wherein the determining comprises:
identifying a plurality of confidence levels associated with the plurality of paths, and
selecting the value of the data generated by one of the plurality of paths based on its respective confidence level.
16. The method of claim 15, wherein the multi-path voting matrix component uses a second machine learning model trained to determine, based on the plurality of confidence levels, the value of the data from the plurality of values.
16. The method of claim 15, wherein the multi-path voting matrix component uses a second machine learning model trained to determine, based on the plurality of confidence levels, the value of the data from the plurality of values.
17. The method of claim 14, wherein:
the decoder component receives the mud pulse signal as a filtered continuous input signal and identifies a highest peak of the filtered continuous input signal within a packet window to determine the value of the data,
the machine learning signal filter component receives the mud pulse signal as an unfiltered continuous input signal and outputs the filtered continuous signal, and
the machine learning decoder component receives the mud pulse signal as the unfiltered continuous input signal, filters the unfiltered continuous input signal, and outputs a decoded bitstream representing the value of the data.
17. The method of claim 14, wherein:
the decoder component receives the mud pulse signal as a filtered continuous input signal and identifies a highest peak of the filtered continuous input signal within a packet window to determine the value of the data,
the machine learning signal filter component receives the mud pulse signal as an unfiltered continuous input signal and outputs the filtered continuous signal, and
the machine learning decoder component receives the mud pulse signal as the unfiltered continuous input signal, filters the unfiltered continuous input signal, and outputs a decoded bitstream representing the value of the data.
18. The method of claim 1, further comprising controlling, based on the value of the data, the MWD tool to modify an operating parameter in real-time or near real-time, wherein the operating parameter comprises a pulse width of transmitted telemetry signals, a data rate of the transmitted telemetry signals, or some combination thereof.
5. The method of claim 1, further comprising controlling, based on the value of the data, the MWD tool to modify an operating parameter in real-time or near real-time, wherein the operating parameter comprises a pulse width of transmitted telemetry signals, a data rate of the transmitted telemetry signals, or some combination thereof.
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.
Claim(s) 1-20 is/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 pre-AIA the applicant regards as the invention.
In regards to claim 1, the claim recites in lines 3-4 “wherein the mud pulse signal comprises” and in line 8 “a portion of the mud pulse signal”. The claim defines mud pulse signals in line 1 and a mud pulse signal in line 3. It is unclear if the limitations of lines 3-4 and 8 are referring to the signal defined in line 3 or one of the signals defined in line 1. For this reason, the claim is indefinite. The examiner has interpreted the claim in the following way in order to advance prosecution:
Lines 3-4: “receiving a first mud pulse signal from a measurement while drilling (MWD) tool, wherein the first mud pulse signal comprises data”.
Line 8: “repositioning at least a portion of the first mud pulse signal”.
In regards to claim(s) 2-18, the claim(s) is/are indefinite due to its/their dependency on indefinite claim 1.
In regards to claim 3, lines 6-8 of the claim have the same issues described in the rejection of claim 1 above. For this reason, the claim is indefinite.
In regards to claim 4, the claim recites in line 3 “representing data of the plurality of signals”. Claim 1 defines mud pulse signals and a mud pulse signal. Claim 4 defines a plurality of signals. It is unclear if the limitation of line 3 is referring to the signals defined in claim 1 or to the signals defined in claim 4 or both. For this reason, the claim is indefinite.
In regards to claim 5, lines 4, 6, 8 and 10 of the claim have the same issues described in the rejection of claim 1 above. For this reason, the claim is indefinite.
In regards to claim 8, the claim recites in line 1 “determining the value of the data based on” and in line 4 “how close the value”. Claim 1 defines a value of the data, and claim 7 defines a plurality of values of the data. It is unclear to which of the previously defined values the limitations of line 1 and line 4 are referring. For this reason, the claim is indefinite.
Also, the claim recites in line 2 “associated with the plurality of values of the data” and in line 3 “a majority of the plurality of values”. Claim 1 defines a value of the data, and claim 7 defines a plurality of values of the data. It is unclear if the limitations of line 2 and 3 also include the value defined in claim 1 or only include the values defined in claim 7. For this reason, the claim is indefinite.
The examiner has interpreted the claims 1, 7 and 8 in the following way in order to advance prosecution:
Line 5-6 of claim 1: “decoding, using the trained machine learning model, the data to determine a first value of the data”.
Lines 2-3 of claim 7: “to decode the data to determine a first plurality of values of the data”.
Claim 8: The method of claim 7, further comprising determining the first value of the data based on one or more criteria associated with the first plurality of values of the data, the one or more criteria comprising (i) a majority of the first plurality of values of the data that match, (ii) a weighted sum, (iii) a non- linear evaluation function, (iv) a low-pass filter, (v) a determination of how close the first value of the data is to a prior value, (vi) a fuzzy logic selection, or some combination thereof.
In regards to claim 9, lines 1-4 of the claim have the same issues described in the rejection of claim 1 above. For this reason, the claim is indefinite.
In regards to claim(s) 10-13, the claim(s) is/are indefinite due to its/their dependency on indefinite claim 9.
In regards to claim 10, line 1 of the claim has the same issues described in the rejection of claim 1 above. For this reason, the claim is indefinite.
In regards to claim 11, lines 2-3 of the claim have the same issues described in the rejection of claim 1 above. For this reason, the claim is indefinite.
In regards to claim 12, line 3 of the claim has the same issues described in the rejection of claim 1 above. For this reason, the claim is indefinite.
In regards to claim 13, line 1 of the claim has the same issues described in the rejection of claim 1 above. For this reason, the claim is indefinite.
In regards to claim 14, line 3 of the claim has the same issues described in the rejection of claim 1 above. For this reason, the claim is indefinite.
In regards to claim(s) 15-17, the claim(s) is/are indefinite due to its/their dependency on indefinite claim 14.
In regards to claim 15, the claim recites in line 3 “the value of the data based on” and in line 6 “selecting the value of the data”. Claim 1 defines a value of the data, and claim 15 defines a plurality of values of the data. It is unclear to which of the previously defined values the limitations of line 3 and line 6 are referring. For this reason, the claim is indefinite.
Also, the claim recites in line 4 “based on the plurality of values”. Claim 1 defines a value of the data, and claim 15 defines a plurality of values of the data. It is unclear if the limitations of line 4 also include the value defined in claim 1 or only include the values defined in claim 15. For this reason, the claim is indefinite.
The examiner has interpreted the claims 1 and 15 in the following way in order to advance prosecution:
Line 5-6 of claim 1: “decoding, using the trained machine learning model, the data to determine a first value of the data”.
Claim 15: “The method of claim 14, further comprising:
generating, from the plurality of paths, a first plurality of values of the data; and
determining, using a multi-path voting matrix component, the first value of the data based on the first plurality of values of the data, wherein the determining comprises:
identifying a plurality of confidence levels associated with the plurality of paths, and
selecting the first value of the data generated by one of the plurality of paths based on its respective confidence level”.
In regards to claim(s) 16, the claim(s) is/are indefinite due to its/their dependency on indefinite claim 15.
In regards to claim 16, lines 2-3 have the same issues described in the rejection of claim 15 above. For this reason, the claim is indefinite. The examiner has interpreted the claim in the following way in order to advance prosecution: “to determine, based on the plurality of confidence levels, the first value of the data from the first plurality of values of the data”.
In regards to claim 17, lines 2, 5 and 7 of the claim have the same issues described in the rejection of claim 1 above. For this reason, the claim is indefinite.
In regards to claim 19, the claim has the same issues described in the rejection of claim 1 above. For this reason, the claim is indefinite.
In regards to claim 20, the claim has the same issues described in the rejection of claim 1 above. For this reason, the claim is indefinite.
Allowable Subject Matter
Claim(s) 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under nonstatutory double patenting and under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
In regards to claim 1, Jarrot et al. (US-2022/0213786) teaches a method and a system to decode a signal containing downhole data using trained machine learning [par. 0005 L. 6-10, par. 0027 L. 5-10, par. 0030 L. 1-4, par. 0035 L. 1-6, par. 0040 L. 1-10, par. 0058 L. 5-11].
Soos et al. (US-10,385,684) teaches a method and a system to decode a mud pulse signal using a neural network [col. 4 L. 28-33 and L. 49-50, col. 6 L. 62-65, col. 7 L. 7-11, col. 9 L. 43-45 and L. 53-56, col. 10 L. 46-53].
However, the cited prior art does not teach either by anticipation or combination the following limitations: providing a user interface comprising the value of the data for presentation on a computing device of a user, wherein the user interface comprises a graphical element that enables repositioning at least a portion of the first mud pulse signal into a slot that causes the value of the data to be modified
In regards to claims 2-18, the claim would be allowable due to their dependency on claim 1.
In regards to claims 19-20, the claim would be allowable for the same reasons provided for claim 1 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANKLIN D BALSECA whose telephone number is (571)270-5966. The examiner can normally be reached 6AM-4PM EST M-F.
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/FRANKLIN D BALSECA/Examiner, Art Unit 2688
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