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 .
DETAILED ACTION
2. This action is responsive to the Application filed on 4/11/2024, which is a continuation of 18066249, which was filed on 12/14/2022 and now US Patent 11985194. A filing date 4/11/2024 is acknowledged. The sought benefit of CN application 202211417216.9 (which was filed on 11/14/2022) is acknowledged. Claims 1-20 are pending in this application. Claims 1, 11 are independent claims.
Double Patenting
3. 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, 11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4 of U.S. Patent No. 11985194. Although the claims at issue are not identical, they are not patentably distinct from each other because claims in instant application are broader than claims in patent 11985194. Please note independent claim 1 of Patent 11985194 cites “obtaining, by the data center, usage information of the filter element through the sensor network platform … obtaining, by the pipeline network device management sub-platform, the usage information from the data center, determining a filter element maintenance plan based on the usage information,
and sending the filter element maintenance plan to the data center,” as cited in instant application. And claim 4 of Patent 11985114 cited “system” limitations as claim 11 in instant application. See more analysis details below:
Application 18632345
Patent 11985194
1. A method for maintenance of a filter element at a gas gate station, wherein the method is implemented by an Internet of Things system, and the Internet of Things system includes a user platform, a service platform, a device management platform, a sensor network platform, and an object platform that interact in sequence, the device management platform includes a data center and a pipeline network device management sub-platform, and the method is executed by the device management platform, comprising:
obtaining, by the data center, usage information of the filter element through the sensor network platform, wherein the usage information includes at least one of a cleaning cost and a blockage degree; the cleaning cost being determined by processing the blockage degree, an impurity feature, times of the filter element being cleaned, a usage duration of the filter element, and a replacement cycle based on a cost prediction model, wherein the cost prediction model is a machine learning model;
obtaining, by the pipeline network device management sub-platform, the usage information from the data center, determining a filter element maintenance plan at least based on the cleaning cost in the usage information,
and sending the filter element maintenance plan to the data center; and
sending, by the data center, the filter element maintenance plan to the user platform through the service platform.
1. A method for predicting a filter element replacement at a gate station, wherein the method is implemented by an Internet of Things system for predicting the filter element replacement at the gate station, and the Internet of Things system includes a user platform, a service platform, a device management platform, a sensor network platform, and an object platform that interact in sequence, the device management platform includes a data center and a pipeline network device management sub-platform, and the method is executed by the device management platform, comprising: obtaining, by the data center, usage information of the filter element through the sensor network platform, wherein the usage information at least includes at least one of ventilation efficiency of the filter element, filtered impurity information, and a blockage degree;
obtaining, by the pipeline network device management sub-platform, the usage information from the data center, determining a filter element maintenance plan based on the usage information,
and sending the filter element maintenance plan to the data center; sending, by the data center, the filter element maintenance plan to the user platform through the service platform; and replacing the filter element according to the filter element maintenance plan, wherein the filtered impurity information includes an accumulated amount of impurity filtering; and the determining a filter element maintenance plan based on the usage information includes: obtaining the accumulated amount of impurity filtering; and determining the filter element maintenance plan based on the accumulated amount of impurity filtering; wherein the obtaining the accumulated amount of impurity filtering includes: determining an impurity feature based on an impurity prediction model, and determining the accumulated amount of impurity filtering based on the impurity feature, the impurity prediction model being a machine learning model including: a first feature extraction layer, a gas flow prediction layer, a second feature extraction layer, and an impurity prediction layer, wherein the first feature extraction layer is configured to obtain a first feature by processing usage duration, a diameter, and a usage pressure of the filter element; the gas flow prediction layer is configured to determine a gas flow by processing the first feature, the gas flow being a total amount of gas passing through the filter element within a period of time; the second feature extraction layer is configured to obtain a second feature by processing the gas flow, a gas intake quality, a filtration efficiency, and a filtration precision; and the impurity prediction layer is configured to determine the impurity feature by processing the second feature; wherein the first feature extraction layer and the gas flow prediction layer are obtained through joint training based on first training samples and a first label, wherein the first training samples include historical usage duration of sample filter element, diameter of the sample filter element, and usage pressure of the sample filter element and the first label includes an actual gas flow of the sample filter element, the joint training including: inputting the first training samples into an initial first feature extraction layer and obtaining an output of the initial first feature extraction layer; inputting the output of the initial first feature extraction layer into an initial gas flow prediction layer and obtaining an output of the initial gas flow prediction layer; constructing a loss function based on the output of the initial gas flow prediction layer and the first label; updating parameters of the initial first feature extraction layer and the initial gas flow prediction layer iteratively based on the loss function until a first preset condition is met; and obtaining the first feature extraction layer and the gas flow prediction layer; wherein the second feature extraction layer and the impurity prediction layer are obtained through joint training based on second training samples and a second label, wherein the second training samples include historical gas flow of sample filter element, historical gas intake quality of the sample filter element, filtration efficiency of the sample filter element, and filtration precision of the sample filter element, and the second label includes an actual impurity feature of the sample filter element, the joint training including: inputting the second training samples into an initial second feature extraction layer and obtaining an output of the initial second feature extraction layer; inputting the output of the initial second feature extraction layer into an initial impurity prediction layer and obtaining an output of the initial impurity prediction layer; constructing a loss function based on the output of initial impurity prediction layer and the second label; updating parameters of the initial second feature extraction layer and the initial impurity prediction layer iteratively based on the loss function until a second preset condition is met; and obtaining the second feature extraction layer and the impurity prediction layer.
11. An Internet of Things system for maintenance of a filter element at a gas gate station, comprising a user platform, a service platform, a device management platform, a sensor network platform, and an object platform that interact in sequence, wherein the device management platform includes a data center and a pipeline network device management sub-platform, and the device management platform is configured to:
obtain, by the data center, usage information of the filter element through the sensor network platform, wherein the usage information includes at least one of a cleaning cost and a blockage degree; the cleaning cost being determined by processing the blockage degree, an impurity feature, times of the filter element being cleaned, a usage duration of the filter element, and a replacement cycle based on a cost prediction model, wherein the cost prediction model is a machine learning model;
obtain, by the pipeline network device management sub-platform, the usage information from the data center, determine a filter element maintenance plan at least based on the cleaning cost in the usage information, and send the filter element maintenance plan to the data center; and
send, by the data center, the filter element maintenance plan to the user platform through the service platform.
4. An Internet of Things system for predicting a filter element replacement at a gate station for smart gas, comprising a user platform, a service platform, a device management platform, a sensor network platform, and a object platform that interact in sequence, wherein the device management platform includes a data center and a pipeline network device management sub-platform, and the device management platform is configured to: obtain, by the data center, usage information of the filter element through the sensor network platform, wherein the usage information at least includes at least one of ventilation efficiency of the filter element, filtered impurity information, and a blockage degree; obtain, by the pipeline network device management sub-platform, the usage information from the data center, determine a filter element maintenance plan based on the usage information, and send the filter element maintenance plan to the data center; send, by the data center, the filter element maintenance plan to the user platform through the service platform; and replace the filter element according to the filter element maintenance plan wherein the filtered impurity information includes an accumulated amount of impurity filtering; and to determine a filter element maintenance plan based on the usage information, the device management platform is further configured to: obtain the accumulated amount of impurity filtering; and determine the filter element maintenance plan based on the accumulated amount of impurity filtering; wherein to obtain the accumulated amount of impurity filtering, the device management platform is further configured to: determine an impurity feature based on an impurity prediction model, and determine the accumulated amount of impurity filtering based on the impurity feature, the impurity prediction model being a machine learning model including: a first feature extraction layer, a gas flow prediction layer, a second feature extraction layer, and an impurity prediction layer, wherein the first feature extraction layer is configured to obtain a first feature by processing usage duration, a diameter, and a usage pressure of the filter element; the gas flow prediction layer is configured to determine a gas flow by processing the first feature, the gas flow being a total amount of gas passing through the filter element within a period of time; the second feature extraction layer is configured to obtain a second feature by processing the gas flow, a gas intake quality, a filtration efficiency, and a filtration precision; and the impurity prediction layer is configured to determine the impurity feature by processing the second feature; wherein the first feature extraction layer and the gas flow prediction layer are obtained through joint training based on first training samples and a first label, wherein the first training samples include historical usage duration of sample filter element, diameter of the sample filter element, and usage pressure of the sample filter element and the first label includes an actual gas flow of the sample filter element, the joint training including: inputting the first training samples into an initial first feature extraction layer and obtaining an output of the initial first feature extraction layer; inputting the output of the initial first feature extraction layer into an initial gas flow prediction layer and obtaining an output of the initial gas flow prediction layer; constructing a loss function based on the output of the initial gas flow prediction layer and the first label; updating parameters of the initial first feature extraction layer and the initial gas flow prediction layer iteratively based on the loss function until a first preset condition is met; and obtaining the first feature extraction layer and the gas flow prediction layer; wherein the second feature extraction layer and the impurity prediction layer are obtained through joint training based on second training samples and a second label, wherein the second training samples include historical gas flow of sample filter element, historical gas intake quality of the sample filter element, filtration efficiency of the sample filter element, and filtration precision of the sample filter element, and the second label includes an actual impurity feature of the sample filter element, the joint training including: inputting the second training samples into an initial second feature extraction layer and obtaining an output of the initial second feature extraction layer; inputting the output of the initial second feature extraction layer into an initial impurity prediction layer and obtaining an output of the initial impurity prediction layer; constructing a loss function based on the output of initial impurity prediction layer and the second label; updating parameters of the initial second feature extraction layer and the initial impurity prediction layer iteratively based on the loss function until a second preset condition is met; and obtaining the second feature extraction layer and the impurity prediction layer.
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 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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
4. Claims 1-3, 9-13, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lin Fu (US Publication 20230361568 A1, hereinafter Fu), and in view of Richard Carpenter et al (US Publication 20210134075 A1, hereinafter Carpenter).
As for independent claim 1, Fu discloses: A method for maintenance of a filter element at a gas gate station (Abstract, collecting operation and maintenance data of the LNG smart terminal and personnel data of a safety inspector and uploading the collected data to a management platform; monitoring an operation and maintenance situation of the LNG smart terminal in real-time, and generating an inspection order reminder and an inspection instruction according to a preset safety inspection mechanism; [0005], LNG gasification stations), wherein the method is implemented by an Internet of Things system (Abstract, an Internet of Things (IoT) system), and the Internet of Things system includes a user platform, a service platform, a device management platform (Abstract, a management platform), a sensor network platform, and an object platform that interact in sequence, the device management platform includes a data center and a pipeline network device management sub-platform, and the method is executed by the device management platform ([0022], The Internet of Things (IoT) system includes an object platform, a sensing network platform, a management platform, a service platform, and a user platform; the object platform is used to collect the operation and maintenance data of the LNG smart terminal and the personal data of the each safety inspector; the sensing network platform is used to implement a communication connection between the management platform and the object platform for perception and control; the management platform is used to analyze the collected operation and maintenance data and the collected personal data, generate a safety inspection task and send the safety inspection task to the optimal safety inspector for processing, and confirm completion of the safety inspection task; the service platform is used to obtain perception information needed by a user from the management platform for parsing and storage, receive control information issued by the user for processing, and send processed control information to the management platform; and the user platform is used for various types of users to obtain perception information of the LNG smart terminal from the service platform and send the control information to the service platform), comprising:
obtaining, by the data center, usage information of the filter element through the sensor network platform ([0055], The operation and maintenance data includes a number, a location, a put-into-use time, an operation time, and a maintenance time of the LNG smart terminal), … based on a cost prediction model, wherein the cost prediction model is a machine learning model ([0123], The time prediction layer 612 may be a machine learning model for predicting a candidate completion time of a candidate inspection route); obtaining, by the pipeline network device management sub-platform, the usage information from the data center, determining a filter element maintenance plan at least based on the cleaning cost in the usage information, and sending the filter element maintenance plan to the data center ([0126], the route planning layer may be obtained by training a plurality of first training samples with a first label. For example, the plurality of first training samples with the first label may be input into an initial route planning layer, a loss function is constructed through the first label and results of the initial route planning layer, and parameters of the initial route planning layer are iteratively updated based on the loss function. When the loss function of the initial route planning layer satisfies a preset iteration condition, module training is completed, and a trained route planning layer is obtained. The preset iteration condition may be that the loss function converges, or the count of iterations reaches a threshold, or the like); and sending, by the data center, the filter element maintenance plan to the user platform through the service platform ([0022], the service platform is used to obtain perception information needed by a user from the management platform for parsing and storage, receive control information issued by the user for processing, and send processed control information to the management platform; and the user platform is used for various types of users to obtain perception information of the LNG smart terminal from the service platform and send the control information to the service platform).
Fu discloses an Internet of Things system using machine learning to determine a maintenance plan based on the collected usage data but does not clearly disclose the usage data of filter element, in an analogous art of determining a maintenance plan for gas pipelines based on the detected usage data, Carpenter discloses: wherein the usage information includes at least one of a cleaning cost and a blockage degree (Carpenter: [0002], Filters may become loaded or clogged over time requiring cleaning or replacement; [0030], In the example of a fuel filter 118, air filter 124, or oil filter 138, the EOL 222 may occur when the pressure drop across the filter indicates that the filter has become significantly contaminated or clogged and is unacceptably restricting flow; [0032], The current contamination percentage 242 may demarcate the used and the unused capacity of the filter at the time of evaluation and may be estimated within the spectrum or range between a newly installed, uncontaminated filter and an unacceptably contaminated or clogged filter. The measured pressure drop 204 can be converted to a current contamination percentage 242 based on data that relates pressure drop to a percentage of contamination or loading of the filter, which may be provided in a data map of the anticipated performance ratings 196 provided by and determined empirically by the manufacturer); the cleaning cost being determined by processing the blockage degree, an impurity feature, times of the filter element being cleaned, a usage duration of the filter element (Carpenter: [0026], at particular durations of operation), and a replacement cycle (Carpenter: Abstract, A performance data associated with the replaceable maintenance item may be measured and an estimated end of useful life may be determined based on the performance data. The estimated end of useful life is compared to the assigned maintenance interval and, if warranted, the assigned maintenance schedule is modified; [0015], The intervals are typically based on a predetermined number of operating hours or a predetermined duty cycle; [0026], parameter or performance data associated with the replaceable maintenance items at a particular duration of operation or duty cycle);
Fu and Carpenter are analogous arts because they are in the same field of endeavor, determining a maintenance plan for gas pipelines based on the detected usage data. Therefore, it would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Fu using the teachings of Carpenter to include collecting clogging data and replacement data and determining a maintenance schedule based on the detected operation data. It would provide Fu’s method with enhanced capabilities of an optimized maintenance schedule for replaceable maintenance item.
As for claim 2, Fu-Carpenter discloses: wherein training the cost prediction model includes; obtaining first training samples with a first label (Fu: [0127], The first label may include a sample candidate inspection route corresponding to the first training sample); wherein the training samples include historical filtration data and historical cleaning data, and the first label includes the cleaning cost (Fu: [0127], the first training sample may be obtained through historical data); the historical filtration data includes the blockage degree, the impurity feature, and the usage duration of the filter element, the historical cleaning data includes the times of the filter element being cleaned and the replacement cycle (Carpenter: [0030], In the example of a fuel filter 118, air filter 124, or oil filter 138, the EOL 222 may occur when the pressure drop across the filter indicates that the filter has become significantly contaminated or clogged and is unacceptably restricting flow; [0032], The current contamination percentage 242 may demarcate the used and the unused capacity of the filter at the time of evaluation and may be estimated within the spectrum or range between a newly installed, uncontaminated filter and an unacceptably contaminated or clogged filter. The measured pressure drop 204 can be converted to a current contamination percentage 242 based on data that relates pressure drop to a percentage of contamination or loading of the filter, which may be provided in a data map of the anticipated performance ratings 196 provided by and determined empirically by the manufacturer); and inputting the training samples with the first label into an initial cost prediction model, constructing a loss function based on an output of the initial cost prediction model and the first label, updating parameters of the initial cost prediction model iteratively based on the loss function until a first preset condition is met, completing the training and obtaining the cost prediction model (Fu: [0126], the plurality of first training samples with the first label may be input into an initial route planning layer, a loss function is constructed through the first label and results of the initial route planning layer, and parameters of the initial route planning layer are iteratively updated based on the loss function. When the loss function of the initial route planning layer satisfies a preset iteration condition, module training is completed, and a trained route planning layer is obtained. The preset iteration condition may be that the loss function converges, or the count of iterations reaches a threshold, or the like).
As for claim 3, Fu-Carpenter discloses: wherein the obtaining usage information of the filter element includes: obtaining ventilation efficiency of the filter element based on a pressure difference between a gas inlet and a gas outlet of the filter element (Carpenter: [0027], in the matter of a filter such as the fuel filter 118, air filter 124, or oil filter 138, the performance data may be the measured pressure drop 204 or the differential pressure across the filter); and determining the blockage degree based on the ventilation efficiency (Carpenter: [0030], In the example of a fuel filter 118, air filter 124, or oil filter 138, the EOL 222 may occur when the pressure drop across the filter indicates that the filter has become significantly contaminated or clogged and is unacceptably restricting flow; [0032], The current contamination percentage 242 may demarcate the used and the unused capacity of the filter at the time of evaluation and may be estimated within the spectrum or range between a newly installed, uncontaminated filter and an unacceptably contaminated or clogged filter. The measured pressure drop 204 can be converted to a current contamination percentage 242 based on data that relates pressure drop to a percentage of contamination or loading of the filter, which may be provided in a data map of the anticipated performance ratings 196 provided by and determined empirically by the manufacturer).
As for claim 9, Fu-Carpenter discloses: wherein the obtaining usage information of the filter element further includes: determining the blockage degree by processing the impurity feature, a filter medium, and the usage duration of the filter element based on a blockage prediction model, wherein the blockage prediction model is a machine learning model (Fu: [0019], predicting the inspection completion time by processing the personnel features, the terminal features, and the location features through an inspection completion time prediction model, wherein the inspection completion time prediction model is a machine learning model).
As for claim 10, Fu-Carpenter discloses: wherein training the blockage prediction model includes: obtaining fourth training samples with a fourth label, wherein the fourth training samples include an impurity feature, a filter medium, a usage duration of a filter element in historical filter data, and the fourth label includes a blockage degree corresponding to the historical filter data; inputting the fourth training samples into an initial blockage prediction model, constructing a loss function based on an output of the initial blockage prediction and the fourth label, updating parameters of the initial blockage prediction model iteratively based on the loss function until a fourth preset condition is met, completing the training and obtaining the blockage prediction model (Fu: [0126], the route planning layer may be obtained by training a plurality of first training samples with a first label. For example, the plurality of first training samples with the first label may be input into an initial route planning layer, a loss function is constructed through the first label and results of the initial route planning layer, and parameters of the initial route planning layer are iteratively updated based on the loss function. When the loss function of the initial route planning layer satisfies a preset iteration condition, module training is completed, and a trained route planning layer is obtained. The preset iteration condition may be that the loss function converges, or the count of iterations reaches a threshold, or the like).
As per Claim 11, it recites features that are substantially same as those features claimed by Claim 1, thus the rationales for rejecting Claim 1 are incorporated herein.
As per Claim 12, it recites features that are substantially same as those features claimed by Claim 2, thus the rationales for rejecting Claim 2 are incorporated herein.
As per Claim 13, it recites features that are substantially same as those features claimed by Claim 3, thus the rationales for rejecting Claim 3 are incorporated herein.
As per Claim 18, it recites features that are substantially same as those features claimed by Claim 9, thus the rationales for rejecting Claim 9 are incorporated herein.
As per Claim 19, it recites features that are substantially same as those features claimed by Claim 10, thus the rationales for rejecting Claim 10 are incorporated herein.
As per Claim 20, it recites features that are substantially same as those features claimed by Claim 1, thus the rationales for rejecting Claim 1 are incorporated herein.
5. Claims 4-8, 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Fu and Carpenter as applied on Claims 1 and 11, and further in view of Sheng-Chieh Cheng et al (US Publication 20180161709 A1, hereinafter Cheng).
As for claim 4, Fu-Carpenter discloses: and the usage duration of the filter element; and determining the filter element maintenance plan based on the usage duration, the replacement cycle, and the cleaning cost (Carpenter: Abstract, A performance data associated with the replaceable maintenance item may be measured and an estimated end of useful life may be determined based on the performance data. The estimated end of useful life is compared to the assigned maintenance interval and, if warranted, the assigned maintenance schedule is modified), but Fu-Carpenter does note disclose determining accumulated amount of impurity filtering, in another controlling filter maintenance based on the detected data, Cheng discloses: obtaining an accumulated amount of impurity filtering (Cheng: Abstract, obtaining an initial impurity accumulative quantity through the operating model); determining the replacement cycle of the filter element based on the accumulated amount of impurity filtering (Cheng: [0005], an accurate estimation on the time to replace the clogged fluid filter), an accumulated amount threshold,
Fu and Carpenter and Cheng are analogous arts because they are in the same field of endeavor, determining a maintenance plan for gas pipelines based on the detected usage data. Therefore, it would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Fu using the teachings of Cheng to include obtaining impurity accumulative quantity. It would provide Fu’s method with enhanced capabilities of an optimized maintenance schedule for replaceable maintenance item.
As for claim 5, Fu-Carpenter-Cheng discloses: wherein the determining the filter element maintenance plan based on the usage duration, the replacement cycle, and the cleaning cost includes: in response to the usage duration greater than or equal to the replacement cycle, determining the filter element maintenance plan being replacing the filter element; and in response to the usage duration less than the replacement cycle, determining the filter element maintenance plan based on the cleaning cost and a replacement cost (Carpenter: Abstract, A performance data associated with the replaceable maintenance item may be measured and an estimated end of useful life may be determined based on the performance data. The estimated end of useful life is compared to the assigned maintenance interval and, if warranted, the assigned maintenance schedule is modified).
As for claim 6, Fu-Carpenter-Cheng discloses: wherein the obtaining an accumulated amount of impurity filtering includes: determining the impurity feature based on an impurity prediction model, wherein the impurity prediction model is a machine learning model (Fu: [0123], The time prediction layer 612 may be a machine learning model for predicting a candidate completion time of a candidate inspection route); and determining the accumulated amount of impurity filtering based on the impurity feature (Cheng: [0005], an accurate estimation on the time to replace the clogged fluid filter).
As for claim 7, Fu-Carpenter-Cheng discloses: wherein the impurity prediction model includes a first feature extraction layer, a gas flow prediction layer, a second feature extraction layer, and an impurity prediction layer; and the determining the impurity feature based on an impurity prediction model includes: obtaining a first feature by processing the usage duration, a diameter, and a usage pressure of the filter element based on the first feature extraction layer; determining a gas flow by processing the first feature based on the gas flow prediction layer;
obtaining a second feature by processing the gas flow, gas intake quality, filtration efficiency, and filtration precision based on the second feature extraction layer; and determining the impurity feature by processing the second feature based on the impurity prediction layer (Carpenter: [0005], The preventative maintenance system also includes a processor configured to estimate an estimated end of useful life for the replaceable maintenance item based on the performance data, compare the estimated end of useful life to the initially assigned predetermined maintenance interval, and to modify the assigned maintenance schedule if the estimated end of useful life does not match the predetermined maintenance interval; Cheng: Abstract, A method for detecting an abnormality of a fluid filter includes: detecting a flow rate of a fluid in the fluid filter; detecting a pressure difference in the fluid filter; constructing an operating model of the fluid filter in accordance with a geometry of the fluid filter, a physical characteristic of the fluid, a porosity of the fluid filter, an impurity density, the flow rate and the pressure difference; obtaining an initial impurity accumulative quantity through the operating model; estimating a time dependent impurity accumulative status through a Kalman filter in accordance with the initial impurity accumulative quantity and the pressure difference; obtaining an impurity accumulative quantity in an estimated time in accordance with the time dependent impurity accumulative status, and then comparing the impurity accumulative quantity with a pre-determined value to determine if the fluid filter operates normally).
As for claim 8, Fu-Carpenter-Cheng discloses: wherein training the impurity prediction layer includes: jointly training the first feature extraction layer and the gas flow prediction layer, including: inputting second training samples with a second label into an initial first feature extraction layer and obtaining an output of the initial first feature extraction layer; inputting the output of the initial first feature extraction layer into an initial gas flow prediction layer and obtaining an output of the initial gas flow prediction layer; constructing a loss function based on the output of the initial gas flow prediction layer and the second label;
updating parameters of the initial first feature extraction layer and the initial gas flow prediction layer iteratively based on the loss function until a second preset condition is met; and obtaining the first feature extraction layer and the gas flow prediction layer; wherein the second training samples include historical usage duration of a sample filter element, a diameter of the sample filter element, and a usage pressure of the sample filter element, and the second label includes an actual gas flow of the sample filter element; and jointly training the second feature extraction layer and the impurity prediction layer, including: inputting third training samples with a third label into an initial second feature extraction layer and obtaining an output of the initial second feature extraction layer; inputting the output of the initial second feature extraction layer into an initial impurity prediction layer and obtaining an output of the initial impurity prediction layer; constructing a loss function based on the output of initial impurity prediction layer and the third label; updating parameters of the initial second feature extraction layer and the initial impurity prediction layer iteratively based on the loss function until a third preset condition is met; and obtaining the second feature extraction layer and the impurity prediction layer, wherein the third training samples include a historical gas flow of sample filter element, historical gas intake quality of the sample filter element, filtration efficiency of the sample filter element, and filtration precision of the sample filter element, and the third label includes an actual impurity feature of the sample filter element (Fu: [0126], the route planning layer may be obtained by training a plurality of first training samples with a first label. For example, the plurality of first training samples with the first label may be input into an initial route planning layer, a loss function is constructed through the first label and results of the initial route planning layer, and parameters of the initial route planning layer are iteratively updated based on the loss function. When the loss function of the initial route planning layer satisfies a preset iteration condition, module training is completed, and a trained route planning layer is obtained. The preset iteration condition may be that the loss function converges, or the count of iterations reaches a threshold, or the like).
As per Claim 14, it recites features that are substantially same as those features claimed by Claim 4, thus the rationales for rejecting Claim 4 are incorporated herein.
As per Claim 15, it recites features that are substantially same as those features claimed by Claim 6, thus the rationales for rejecting Claim 6 are incorporated herein.
As per Claim 16, it recites features that are substantially same as those features claimed by Claim 7, thus the rationales for rejecting Claim 7 are incorporated herein.
As per Claim 17, it recites features that are substantially same as those features claimed by Claim 8, thus the rationales for rejecting Claim 8 are incorporated herein.
Examiner’s Note
Examiner has cited particular columns/paragraph and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as “Applicants believe no new matter has been introduced” may be deemed insufficient.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Applicants are required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Sun (US Publication 20230116964) SYSTEMS AND METHODS FOR CONTROLLING VARIABLE REFRIGERANT FLOW SYSTEMS AND EQUIPMENT USING ARTIFICIAL INTELLIGENCE MODELS
Takayanagi (US Publication 20120288410) EXHAUST GAS PURIFICATION APPARATUS FOR ENGINE
Dollmeyer (US Publication 20070056272) Apparatus, System, And Method For Determining And Implementing Estimate Reliability
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118
Prosecution Insights