DETAILED ACTION
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 .
Status of Claims
Claims 1-19 are currently pending in application 19/232,845.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/9/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is 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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form 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 http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-7 of U.S. Patent No. 12,361,506 (18/920,857). Although the claims at issue are not identical, they are not patentably distinct from each other because both inventions disclose equivalent elements for a connected gas-emergency management system that helps authorities and gas companies respond more intelligently to incidents like leaks, fires, shutdowns, or supply interruptions. Both inventions receive emergency event information from gas monitoring equipment, such as location, time, severity, and affected area. Both inventions claim a system that then looks up a matching emergency response plan from a stored database. Both inventions also check live traffic and environmental conditions, plus available staff and resources. Using that updated information, both inventions revise the original plan into a target emergency response plan tailored to the current situation. The target plan is sent to the gas company for execution. The plan can also be pushed to dispatch personnel terminals so responders know what to do. In addition, both inventions claim a system that can generate control instructions for gas regulation facilities. Those facilities can adjust pipeline pressure and control the duration or range of gas reduction or suspension. Finally, both inventions claim a system that can also learn from historical emergency data and update its emergency plan database over time. See Claim Chart Below.
US 19/232,845
US 12,361,506 (18/920,857)
Independent Claims 1, 10, and 19
An Internet of Things (IoT) system for intelligent gas emergency safety management (A method for intelligent gas emergency safety management, the method being realized by an IoT system for intelligent gas emergency safety management; A non-transitory computer-readable storage medium, wherein the storage medium stores computer instructions), comprising:
a government safety supervision service platform, a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, and a gas equipment object platform; wherein the government safety supervision object platform includes a gas company management platform;
the government safety supervision management platform is configured to:
obtain, based on the gas company management platform, emergency event information from the gas equipment object platform;
determine an initial emergency response plan by matching in an emergency response plan database based on the emergency event information;
obtain traffic information and environmental information within an emergency scope from the government safety supervision service platform;
obtain gas supply and demand data, available personnel information, and available resources information from the gas company management platform;
determine a target emergency response plan by updating the initial emergency response plan based on the traffic information, the environmental information, the gas supply and demand data, the available personnel information, and the available resources information; and
send the target emergency response plan to the gas company management platform for execution through the government safety supervision sensor network platform, and obtain an execution result of the gas company management platform;
the gas company management platform is configured to:
send the target emergency response plan to a terminal of dispatch personnel involved in the target emergency response plan;
generate a control instruction based on a pressure regulation parameter, and send the control instruction to a corresponding gas regulation facility; and
control the regulation facility to regulate a pressure control range and a time period of one or more pipelines in a gas pipeline network.
6. The system of claim 1, wherein
the government safety supervision management platform is further configured to:
periodically obtain emergency feedback information from the gas equipment object platform after an occurrence of a gas emergency event;
update the emergency scope corresponding to the gas emergency event based on the emergency event information and the emergency feedback information;
obtain traffic information and environmental information within an updated emergency scope; and
update the target emergency response plan based on the traffic information and the environmental information within the updated emergency scope.
7. The system of claim 6, wherein
the government safety supervision management platform is further configured to:
determine the updated emergency scope through a prediction model based on the emergency event information and the emergency feedback information, the prediction model being a machine learning model.
8. The system of claim 7, wherein
the prediction model includes a severity update layer and an emergency scope prediction layer; the severity update layer is configured to determine an updated estimated severity level corresponding to the emergency event information based on the emergency event information and the emergency feedback information; and
the emergency scope prediction layer is configured to determine the updated emergency scope based on the updated estimated severity level and the emergency feedback information.
9. The system of claim 8,
wherein the severity update layer and the emergency scope prediction layer are both neural networks, and the prediction model is obtained through a first stage of training; the first stage of training includes: sequentially training an initial prediction model based on a first training dataset, validating the initial prediction model based on a first validation dataset, and testing the initial prediction model based on a first test dataset to obtain the prediction model;
wherein the first training dataset, the first test dataset, and the first validation dataset are derived from an event dataset corresponding to historical gas emergency events, the event dataset includes the emergency event information and the emergency feedback information;
data in the first training dataset, the first test dataset, and the first validation dataset are in a first predetermined ratio;
there is no data overlap between the first training dataset, the first test dataset, and the first validation dataset, and a statistical difference of samples of the first training dataset is greater than a predetermined difference threshold, the predetermined difference threshold being related to a statistical value of severity levels of the historical gas emergency events.
2. The system of claim 1, wherein the government safety supervision management platform is further configured to: obtain historical emergency data through the gas company management platform, the historical emergency data including an emergency response plan for resolving a historical gas emergency event; and
update the emergency response plan database based on the historical emergency data.
3. The system of claim 2, wherein the government safety supervision management platform is further configured to: predict an estimated effect of the emergency response plan in the historical emergency data based on an emergency event information sequence and an emergency feedback information sequence during the execution of the emergency response plan; and update the emergency response plan database based on the estimated effect and a score threshold for the emergency response plan.
4. The system of claim 3, wherein the government safety supervision management platform is further configured to: obtain a predicted severity level sequence during the execution of the emergency response plan by the severity update layer based on the emergency event information sequence and the emergency feedback information sequence; and predict the estimated effect based on the predicted severity level sequence.
5. The system of claim 3, wherein the score threshold is related to frequencies of gas emergency events in different regions.
Independent Claims 1, 6, and 7
An Internet of Things (IoT) system for intelligent gas emergency safety management (A method for intelligent gas emergency safety management, the method being realized by an IoT system for intelligent gas emergency safety management; A non-transitory computer-readable storage medium, wherein the storage medium stores computer instructions), comprising:
a government safety supervision service platform, a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, and a gas equipment object platform; wherein the government safety supervision object platform includes a gas company management platform;
the government safety supervision management platform is configured to:
obtain, based on the gas company management platform, emergency event information from the gas equipment object platform;
determine an initial emergency response plan by matching in an emergency response plan database based on the emergency event information;
obtain traffic information and environmental information within an emergency scope from the government safety supervision service platform;
obtain gas supply and demand data, available personnel information, and available resources information from the gas company management platform;
determine a target emergency response plan by updating the initial emergency response plan based on the traffic information, the environmental information, the gas supply and demand data, the available personnel information, and the available resources information; and
send the target emergency response plan to the gas company management platform for execution through the government safety supervision sensor network platform, and obtain an execution result of the gas company management platform;
the government safety supervision management platform is further configured to:
periodically obtain emergency feedback information from the gas equipment object platform after an occurrence of a gas emergency event;
update the emergency scope corresponding to the gas emergency event based on the emergency event information and the emergency feedback information;
obtain traffic information and environmental information within an updated emergency scope; and
update the target emergency response plan based on the traffic information and the environmental information within the updated emergency scope;
the government safety supervision management platform is further configured to:
determine the updated emergency scope through a prediction model based on the emergency event information and the emergency feedback information, wherein the prediction model is a machine learning model;
the prediction model includes a severity update layer and an emergency scope prediction layer; an input of the severity update layer includes the emergency event information and the emergency feedback information, and an output of the severity update layer includes an updated estimated severity level;
an input of the emergency scope prediction layer includes the emergency feedback information and the updated estimated severity level, and an output of the emergency scope prediction layer includes the updated emergency scope;
the prediction model is obtained through a first stage of training; the first stage of training includes: training an initial prediction model based on a first training dataset,
validating the initial prediction model based on a first validation dataset, and testing the initial prediction model based on a first test dataset to obtain the prediction model;
wherein the first training dataset, the first test dataset, and the first validation dataset are derived from an event dataset corresponding to historical gas emergency events, the event dataset includes the emergency event information and the emergency feedback information;
a data volume of the first training dataset, a data volume of the first test dataset, and a data volume of the first validation dataset are in a first predetermined ratio;
there is no data overlap between the first training dataset, the first test dataset, and the first validation dataset, and a statistical difference of samples of the first training dataset is greater than a predetermined difference threshold, the predetermined difference threshold being related to a statistical value of severity levels of the historical gas emergency events.
2. The system of claim 1, wherein the government safety supervision management platform is further configured to: obtain historical emergency data through the gas company management platform; and
update the emergency response plan database based on the historical emergency data.
3. The system of claim 2, wherein the government safety supervision management platform is further configured to: predict an estimated effect of an emergency response plan in the historical emergency data based on an emergency event information sequence and an emergency feedback information sequence during the execution of the emergency response plan; and update the emergency response plan database based on the estimated effect and a score threshold for the emergency response plan.
4. The system of claim 3, wherein the government safety supervision management platform is further configured to: obtain a predicted severity level sequence during the execution of the emergency response plan by the severity update layer based on the emergency event information sequence and the emergency feedback information sequence; and predict the estimated effect based on the predicted severity level sequence.
5. The system of claim 3, wherein the score threshold is related to occurrence frequencies of gas emergency events in different regions.
Claim Rejections - 35 USC § 102
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 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.
Claims 1-5, 10-14, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shao et al. (US 2023/0252877 A1).
As per independent Claim 1, 10, and 19, Shao discloses an Internet of Things (IoT) system for intelligent gas emergency safety management (a method for intelligent gas emergency safety management, the method being realized by an IoT system for intelligent gas emergency safety management; a non-transitory computer-readable storage medium, wherein the storage medium stores computer instructions), comprising: a government safety supervision service platform, a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, and a gas equipment object platform (See at least Fig.1); wherein the government safety supervision object platform includes a gas company management platform;
the government safety supervision management platform is configured to:
obtain, based on the gas company management platform, emergency event information from the gas equipment object platform (See at least Para 0030, “… As another example, the smart gas management platform may obtain the information of the gas emergency through a client, a storage device inside or outside IoT system for handling an emergency based on a call center of smart gas, etc.”; See also );
determine an initial emergency response plan by matching in an emergency response plan database based on the emergency event information (See at least Para 0042);
obtain traffic information and environmental information within an emergency scope from the government safety supervision service platform (See at least Para 0077, “The environmental information 330 may refer to information related to an environment when the gas leakage event occurs. For example, the environmental information 330 may include a geographical environment (e.g., an altitude), a weather environment (e.g., a wind direction, a wind speed, a gas pressure, a temperature, etc.), a surrounding building environment (e.g., a building type, a building density, a building height, and an occupancy rate), or the like, or any combination thereof.”);
obtain gas supply and demand data, available personnel information, and available resources information from the gas company management platform (See at least Para 0031-0042);
determine a target emergency response plan by updating the initial emergency response plan based on the traffic information, the environmental information, the gas supply and demand data, the available personnel information, and the available resources information (See at least Para 0031-0042); and
send the target emergency response plan to the gas company management platform for execution through the government safety supervision sensor network platform, and obtain an execution result of the gas company management platform (See at least Para 0018, “The government user sub-platform may provide data related to gas operation for a government user. The government user sub-platform may dispatch personnel, materials, equipment, etc. based on the emergency response plan of the gas emergency to implement the emergency response plan of the gas emergency. The government user sub-platform may correspond to and interact with a smart operation service sub-platform to obtain a gas operation service. The supervision user sub-platform may supervise operation of the entire IoT system for handling an emergency based on a call center of smart gas for a supervision user. The supervision user sub-platform may correspond to and interact with a smart supervision service sub-platform to obtain a safety supervision service. The smart gas user platform may perform a bidirectional interaction with the smart gas service platform downwards, send a query instruction related to the emergency response plan of the gas emergency to the smart gas service platform, receive the emergency response plan of the gas emergency uploaded by the smart gas service platform, etc.”; Para 0043-0046);
the gas company management platform is configured to:
send the target emergency response plan to a terminal of dispatch personnel involved in the target emergency response plan (See at least Para 0018-0019);
generate a control instruction based on a pressure regulation parameter, and send the control instruction to a corresponding gas regulation facility (See at least Para 0043); and
control the regulation facility to regulate a pressure control range and a time period of one or more pipelines in a gas pipeline network (See at least Para 0043).
As per Claims 2 and 11, Shao discloses wherein the government safety supervision management platform is further configured to: obtain historical emergency data through the gas company management platform, the historical emergency data including an emergency response plan for resolving a historical gas emergency event; and update the emergency response plan database based on the historical emergency data (See at least Para 0033, Para 0073).
As per Claims 3 and 12, Shao discloses wherein the government safety supervision management platform is further configured to: predict an estimated effect of the emergency response plan in the historical emergency data based on an emergency event information sequence and an emergency feedback information sequence during the execution of the emergency response plan; and update the emergency response plan database based on the estimated effect and a score threshold for the emergency response plan (See at least Para 0033, Para 0073).
As per Claims 4 and 13, Shao discloses wherein the government safety supervision management platform is further configured to: obtain a predicted severity level sequence during the execution of the emergency response plan by the severity update layer based on the emergency event information sequence and the emergency feedback information sequence; and predict the estimated effect based on the predicted severity level sequence (See at least Para 0031-0042).
As per Claims 5 and 14, Shao discloses wherein the score threshold is related to frequencies of gas emergency events in different regions (See at least Para 0031-0043).
Allowable Subject Matter
Claims 6-9 and 15-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten (as a whole) in independent form including all of the limitations of the base claims and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of References Cited. The Examiner suggests the applicant review all of these documents before submitting any amendments.
Martin et al. (US 2022/0303380 A1) – Systems and Methods for automated emergency response (See at least Para 0007, “Another advantage provided by the systems, servers, devices, methods, and media of the instant application is the ability to automatically coordinate ESPs for responding to emergencies. Different emergencies require different forms of emergency response, often times based on the nature of the emergency (e.g., fire or medical) and the severity of the emergency. In some embodiments, when a person generates an emergency alert for an emergency (such as by dialing 9-1-1 on a mobile phone in the United States) using a smartphone or other IoT device, an emergency management system (EMS) gathers emergency data, determines a nature of the emergency, determines a severity of the emergency, and, based on the nature and severity of the emergency, generates a dispatch recommendation suggesting the recommended form of emergency response for the emergency. In some embodiments, the EMS gathers emergency data through sensors housed within the device or other devices associated with the emergency.”; See also Para 0008 and Para 0129)
Shao et al (CN 116386287 A) – Shao discloses a gas leak intelligent warning method for smart gas (See at least Para 0005-0007, Contents of the Invention, “The specification one or more embodiments provide a gas leakage intelligent pre-warning method for intelligent gas. The method is performed by the intelligent gas indoor security management sub-platform, comprising: obtaining the monitoring information and user description information, wherein the monitoring information comprises alarm information and gas terminal monitoring data, the user description information comprises user-defined alarm information uploaded by the user; based on the monitoring information and the user description information, determining fuel gas leakage reason; determining a candidate solution based on the fuel gas leakage reason; determining model to the gas leakage reason and the candidate solution based on the effective solution, determining the effective solution of the candidate solution, determining the target solution in the candidate solution according to the effective solution, wherein the effective solution determining model is a machine learning model. The specification one or more embodiments provide a gas leakage intelligent pre-warning internet of things system for intelligent gas, the internet of things system comprises an intelligent gas user platform, intelligent gas service platform, intelligent gas safety management platform, intelligent fuel gas sensing network platform and intelligent gas object platform, the intelligent gas safety management platform comprises an intelligent gas indoor security management platform and intelligent gas data centre, the intelligent gas indoor security management platform is configured to perform the following operations: obtaining the monitoring information and user description information, wherein the monitoring information comprises alarm information and gas terminal monitoring data, the user description information comprises user-defined alarm information uploaded by the user; based on the monitoring information and the user description information, determining fuel gas leakage reason; determining a candidate solution based on the fuel gas leakage reason; determining model to the gas leakage reason and the candidate solution based on the effective solution, determining the effective solution of the candidate solution, determining the target solution in the candidate solution according to the effective solution, wherein the effective solution determining model is a machine learning model. One or more embodiments of the specification provides a gas leakage intelligent pre-warning device for intelligent gas, comprising a processor, the processor is used for executing the intelligent gas leakage intelligent pre-warning method for intelligent gas. One or more embodiments of the specification provides a computer readable storage medium, the storage medium stores computer instructions, when the computer reads the computer instructions in the storage medium, the computer executes the intelligent gas leakage intelligent early warning method for intelligent gas.”)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN P OUELLETTE whose telephone number is (571)272-6807. The examiner can normally be reached on M-F 8am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda C Jasmin, can be reached at telephone number (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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August 13, 2026
/JONATHAN P OUELLETTE/Primary Examiner, Art Unit 3629
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