DETAILED ACTION
This is in response to the application filed on December 18th 2024, in which claims 1-20 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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/23/25 and 1/22/25 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6 of U.S. Patent No. 12,219,014 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims from the ‘014 reference patent anticipated the pending claims.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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) 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Munoz US 2021/0174973 A1.
Regarding claim 1, Munoz discloses: A method for smart control of a collection terminal, the method being executed by a smart gas device management platform of an Internet of Things (IoT) system (IoT system for sensing and managing gas pipeline – abstract, paragraphs 2, 110 and Fig. 18), and the method comprising:
receiving,… gas data collected by a gas pipeline network data collection terminal and environmental monitoring data collected by an environmental monitoring device (sensors transmit data monitoring gas pipeline and environment – paragraphs 69, 70, 112 and Figs. 3, 15), wherein the gas data includes gas pressure data, gas flow data, and gas temperature data (data includes pressure and temperature – paragraph 44; and flow rates – paragraphs 69 and 112), and the environmental monitoring data includes environmental temperature data, environmental visibility data, and environmental humidity data (measure environmental phenomena including temperature, humidity and rainfall, pollution and particulate matter which are equivalent to “visibility data” – paragraph 112);
in response to determining that at least one of the gas data or the environmental monitoring data does not meet a first preset condition (this is a condition precedent, the condition being “at least one of the gas data or the environmental monitoring data does not meet a first preset condition; thus everything occurring in response to the condition being met is not required by the BRI of the claim because these are “contingent limitations” that are not required to be performed when the condition is not met – see MPEP 2111.04 for more information), generating a first adjustment parameter; sending the first adjustment parameter to one or more first associated gas pipeline network data collection terminals of the gas pipeline network data collection terminal; predicting, through an environmental data prediction model, future environmental change data at a future time point for an environment in which the gas pipeline network data collection terminal is located based on historical gas data and current gas data of the gas pipeline network data collection terminal, and historical environmental monitoring data and current environmental monitoring data collected by the environmental monitoring device, wherein the environmental data prediction model is a machine learning model; in response to determining that the future environmental change data does not meet a second preset condition, determining a gas pipeline network data collection terminal to be adjusted; generating a second adjustment parameter based on the future environmental change data; and sending the second adjustment parameter to the gas pipeline network data collection terminal to be adjusted (even though these features are not required by the BRI of the claim, for purposes of compact prosecution, applicant is advised that Munoz discloses at least generating and sending adjustment parameters for a gas pipeline network via process control and predictive maintenance – see paragraphs 110; Munoz also discloses an environmental prediction model which is a machine learning model – paragraphs 35, 116, 133; and sending commands to the gas pipeline network for adjusting parameters – perform process control in response to analyzing sensor data – abstract, paragraphs 74, 93, 110).
Munoz does not explicitly disclose receiving data based on a preset transmission cycle but this would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention. It is very well-known in the art to receive sensor data either upon command (i.e. pull style) or at regular occurring intervals (e.g. push). Thus, this is merely the combination of a very well-known technique according to its established function in order to yield a predictable result.
Regarding claim 2, Munoz discloses an input of the environmental data prediction model includes time data and a target time period, the time data is a current time point, and the target time period is a set future time period (provide real time sensor information – paragraph 15; sensor information is used to train model – paragraph 67; the system is “predictive” and thus considers a target time in a future time period – see paragraphs 72-73).
Regarding claim 3, Munoz discloses the environmental prediction model is trained based on training samples with labels (model / artificial intelligence learns base state in order to predict maintenance and abnormal states – paragraphs 15, 34 and 133). Furthermore, the use of labels in machine learning is well-known in the art as supervised learning. Munoz also discloses the training samples include sample gas data, sample environmental monitoring data, a first historical time point, and a sample target time period, and the labels include actual environmental change data for a second historical time point corresponding to each of the second training samples, wherein the first historical time point precedes the second historical time point, and the second historical time is separated from the first historical time point by the sample target time period (sensors provide time sampled data, including gas and environment data for the purpose of improving the model – paragraphs 35, 67, 72 and 133).
Regarding claim 4, Munoz discloses the input of the environmental data prediction model further includes associated gas data and associated environmental monitoring data (sensor readings are uploaded to model to improve accuracy – paragraph 133).
Regarding claim 5, Munoz discloses in response to determining that the gas pipeline network data collection terminal receives the first adjustment parameter and the second adjustment parameter simultaneously, adjusting an operational parameter of the gas pipeline network data collection terminal based on the first adjustment parameter and the second adjustment parameter (as explained above in the rejection of claim 1, this is a condition – the parameters arriving simultaneously - that may never occur, thus the broadest reasonable interpretation of this claim does not require the following features because they are contingent limitations; applicant is advised that Munoz teaches adjusting operational parameters of the gas pipeline network as explained above).
Allowable Subject Matter
Claims 6-11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the claims recite the same subject matter as the allowed parent application.
Claims 12-20 appear to be allowable upon overcoming the double patenting rejection. Although claim 12 corresponds to claim 1 which is currently rejected, the scope of claim 12 is different because it is a system claim and not a method/process claim. Claim 12 also contains additional features regarding the preset transmission cycle.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
W. Z. Khan, M. Y. Aalsalem, M. K. Khan, M. S. Hossain and M. Atiquzzaman, "A reliable Internet of Things based architecture for oil and gas industry," 2017 19th International Conference on Advanced Communication Technology (ICACT), PyeongChang, Korea (South), 2017, pp. 705-710; discloses monitoring pipelines using an IoT system with sensors and computer control to adjust operations in response to anomaly detection (see abstract, Sections I, II and III).
S. Ahmed, F. Le Mouël and N. Stouls, "Resilient loT-based Monitoring System for Crude Oil Pipelines," 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), Paris, France, 2020, pp. 1-7; discloses an loT based monitoring system for pipeline networks (abstract).
M. D. Y. Moussa, A. M. Aibinu, A. Abdurrahman, K. O. Shobowale and A. J. Chikezie, "Smart Pipeline Monitoring System: A Review," 2023 International Conference on Energy, Power, Environment, Control, and Computing (ICEPECC), Gujrat, Pakistan, 2023, pp. 1-6; discloses a smart monitoring system for a gas pipeline network that provides real-time capability and prediction capability (abstract).
Wang et al. US 2023/0324234 A1 discloses a technique for detecting anomalies including the use of a neural network (abstract), the system can be employed to a variety of areas including oil and gas pipelines to provide monitoring (paragraph 3); ultimately the model can determine abnormal gas pipeline data (see paragraphs 64-75).
Cella et al. US 2018/0284758 A1 discloses using sensors to monitor an industrial environment to detect anomalous conditions (abstract, Fig. 1).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON D RECEK whose telephone number is (571)270-1975. The examiner can normally be reached Flex M-F 9-5.
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/JASON D RECEK/Primary Examiner, Art Unit 2458
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