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, 3 – 4, 14 and 16 – 18 have been amended and are hereby entered.
Claims 2, 7 – 8, 10, 13 and 15 were cancelled, while claims 20 – 22 were added.
Claims 1, 3 - 6, 9, 11 - 12, 14, 16 - 22 are pending and have been examined.
This action is made FINAL.
Response to Arguments
Applicant's arguments filed on August 12, 2026 have been fully considered but they are not persuasive.
Amendments and arguments in pp. 16 – 17 from Remarks regarding the 112(b) rejection have been entered and considered. Therefore, this rejection has been withdrawn due to the applicant's amendments.
Regarding the applicant's arguments against the 101 rejection of pending claims on pages 19 -45: Applicant’s arguments directed to 101 analysis were considered. However, these arguments are not persuasive and the Examiner respectfully disagrees for the following reasons:
For Step 2A-Prong 1 starting in p. 19: The Applicant argues that the pending claims are not directed to any of the abstract ideas identified because Applicant is comparing the claim limitations directed in part of “collecting” user and pipeline feature data and the “determining” abnormal gas users while reciting computer components responsible of collecting such data and a “prediction model” used and trained by the computer system to determine such abnormal data with claim 1 Example 39 (Training Method for Neural Network for Face Detection). Also, Applicant alleges that such “computations and graph-based iterative machine learning training are rooted in computer science and advanced data analytics, and are practically impossible to perform in the human mind or with pen and paper”. However, the Examiner finds this argument unpersuasive and respectfully disagrees. Because these steps which still recites the abstract idea of a mental process for collecting gas feature data and determining abnormal gas users, since at least such determination encompass observations, evaluations and judgements and a human could still perform such determinations mentally and/or with pen and paper or can be performed by humans without or with the assistance (e.g. tool) a computer. Because the steps do not negate and further still reads in the mental nature of the limitation(s), when clustering and determining such user/pipeline network transportation information and potential/target abnormal gas user, as well as the concept is merely claimed to be performed on a generic computer and is merely using a computer as a tool to perform the concept of sending early warning messages to target abnormal user(s) based on their gas usage among other features (see MPEP 2106.04(a)(2)(III)(B & C)).
Also, these steps fall under the abstract idea of a certain method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) in the form of “fundamental economic principles or practices” since collecting a user gas usage/utility data to determine if the user has abnormal gas usage levels in the data by early flagging or warning the “smart gas service” providers and the targeted user via text or voice messages allows to achieve the end result of successfully avoid or mitigate economic risks for a business user when providing the gas product supplied and services provided to a user that might need to be prevented from “stealing” gas (see ¶0003 from Applicant disclosure). Finally, the hardware components responsible for collecting feature data and the use of the “prediction model” being trained for abnormal gas users determination were further considered at Step 2A Prong 2 but did not render the claim limitations as being significantly more, rather they invoke “apply it” using a computer as a tool to achieve these functions.
For Step 2A-Prong 2 and Step 2B starting in p. 25: The Applicant alleges that the claims integrate, the judicial exception identified, into a practical application and further compares it to claim 2 from Example 45 (Injection Molding Mold Controller), and alleges that the claims 1 and 14 (see pp. 41 – 45 from Remarks also) integrates the judicial exception because it improves the prior art of “gas data anomaly processing by "sending an instruction/signal (the early warning message) based on the judicial exception (the target abnormal user) to control a device (the terminal device) to perform a physical operation (e.g., controlling the terminal device to issue the early warning message in text/voice form)”. However, the Examiner finds these arguments unpersuasive and respectfully disagrees. Because such “controlling” of a terminal device as claimed is simply sending instructions to issue early warning messages in text/voice formats which is considered really nothing more than links to computer for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components (refer to MPEP 2106.05 f (2)).
Regarding to Applicant arguments in pp. 33 – 35 directed to the claims “defining a specific machine learning prediction model (a graph model) and explicitly converting the input data structure into an "outlier user distribution map," where the node and edge features (historical maintenance data, pipeline branches, distances, etc.) are customized for the gas pipeline network, it achieves an improvement in computer functionality” is unpersuasive and the Examiner respectfully disagrees. Because as the Applicant alleges, these limitations, directed to the generation of graphical model (i.e. histogram distributions) with the cluster results for the computer that further uses a prediction model trained with these data to determine abnormal gas users and their behavior, are still improving the abstract idea itself for the determination of gas usage related data, rather than improving the computer or technical field (i.e. “gas data anomaly processing” systems, as alleged). Further, the computer and ML model used are recited at a high level of generality that is being used as a tool to perform the generic computer functions for storing, monitoring, collecting and obtaining user and gas pipeline network transportation features data as clusters or groups to determine potential/target abnormal gas users to generate and transmit them early warning messages. (See MPEP 2106.05(f)).
Regarding Step 2B arguments in pp. 36 – 39 from are considered unpersuasive and the Examiner respectfully disagrees. Because, these claim limitations and their additional elements, individually and in combination, as discussed above, are not “significantly more” as these are recited in a high level of generality that cannot provide an inventive concept at Step 2B, and are not integrating the abstract idea into a practical application. (see MPEP 2106.05).
Finally, per arguments in pp. 39 – 41 that the Examiner did not provide proof with “clear and convincing evidence" for “unconventional, technical features” identified in the additional elements of the claims (see Berkheimer memo and MPEP 2106.05(d)(I)(2)) and did not consider that the claims did not recite “significantly more than a judicial exception” by reciting additional elements that were deemed to be “well-understood, routine and conventional”, which requires a factual determination to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity (see Berkheimer memo and MPEP 2106.05(d)(I)(2)). Rather, the Examiner considered the claims to be invoking the use of a generic computer (e.g. “apply it”) or reciting mere instructions to implement an abstract idea on a computer used as a tool (see MPEP 2106.05 (f)), as further explained above. Thus, for all the reasons stated above, the Examiner respectfully disagrees, and maintains 35 USC § 101 rejection for these pending claims.
Regarding to Applicant's arguments of rejection under 35 USC § 103 for the pending claims on pages 45 – 61: Applicant’s arguments with respect to the cited prior art against the claims have been considered but are moot due to Applicant amendments that further specify the claimed limitations and the combination of prior art cited principally failed in focusing on the specific step of “constructing” the loss function for the prediction model to be trained with updated parameters as well as the specific process of obtaining the clustering results with specific criteria and conditions. Finally, the latest prior art searched was not close enough to fully satisfy the disclosed claims of the invention, thus nothing else of record renders the missing elements as obvious. Therefore, the 35 USC § 103 rejection has been withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3 - 6, 9, 11 - 12, 14, 16 - 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, although the claim(s) mentioned above failed step 1, all claims were further evaluated for the purpose of compact prosecution. The analysis of this claimed invention recited in the claims begins in view of independent claim 1, the most representative claim of the independent claims set 1, 17 and 19, as follows:
At Step 1: Claims 1 – 16 and 20 - 21 falls under statutory category of a process, while claims 17 – 18 and 22 are directed to a machine and claim 19 is considered an article of manufacture.
At Step 2A Prong 1: Claim 1 (representative of claims 17 and 19) recites an abstract idea in the following limitations:
…
collecting…a user feature and a pipeline network transportation feature of each of a plurality of gas users…;
obtaining a first clustering result and a second clustering result by clustering the gas user based on the user feature and the pipeline network transportation feature respectively, including:
obtaining the first clustering result by clustering the plurality of gas users based on gas consumption behavior similarity, and obtaining the second clustering result by clustering the plurality of gas users based on physical pipeline connectivity;
wherein a clustering parameter corresponding to the second clustering result includes at least one of a complexity degree of a pipeline and whether the pipeline belongs to a same branch;
the first clustering result and the second clustering result including one or more gas user clusters, respectively;
for any one of the gas user clusters:
determining, based on device use data and/or gas metering data of the gas user in the gas user clusters, a potential abnormal gas user; wherein the device use data includes…and a gas usage..., and the gas metering data include a cumulative gas usage value of a plurality of moments; and the potential abnormal gas user includes a first abnormal user and a second abnormal user;
wherein, for the one or more gas user clusters in the first clustering result, the first abnormal user is determined by: generating a plurality of histogram distributions respectively based on a plurality of preset gas use features;
determining one or more outlier users in the plurality of histogram distributions for any one of the plurality of histogram distributions;
counting a number of times each gas user in the first clustering result is determined as the outlier user in the plurality of histogram distributions; wherein the counting a number of times each gas user in the first clustering result is determined as the outlier user in the plurality of histogram distributions comprises:
determining, at least based on the number of times, the first abnormal user in the gas user cluster…
…constructing a loss function based on the first labels and an output…, iteratively updating parameters…based on the loss function until the loss function satisfies a preset condition…wherein the first training samples include histogram distributions of sample preset gas use features and sample outlier user distributions, and the first labels are obtained by labeling sample users that are actually determined as abnormal in history;
determining a target abnormal user based on the first abnormal user and the second abnormal user…
generating…an early warning message based on the target abnormal user,
transmitting the early warning message...
delivering the early warning message…and…
Generally, and as disclosed in the specification in ¶0004, this claimed invention provides “a method, an Internet of Things (IoT) system, and a storage medium for a smart gas abnormal data analysis for quickly and accurately determining an anomalous user and performing a timely warning.” However, the abstract idea(s) of mental processes are/is recited in claim 1 as the limitation steps can be practically be performed in the human mind or in pen and paper (See MPEP 2106.04(a)(2), subsection III). Specifically, the abstract idea is recited in the steps of “…clustering the gas user based on the user feature and the pipeline network transportation feature”, “determining…a potential abnormal gas user”, and “determining a target abnormal user based on the first abnormal user and the second abnormal user…”. Because clustering or grouping user and gas pipeline features to determine potential and target abnormal gas users at least encompasses observations, evaluations and judgements. Also, these steps can either be done with the help of physical aid such as pen and paper or can be performed by humans without or with the assistance (e.g. tool) a computer. Thus, the steps do not negate and further still reads in the mental nature of the limitation(s), when clustering and determining such user/pipeline network transportation information and potential/target abnormal gas user, as well as the concept is merely claimed to be performed on a generic computer and is merely using a computer as a tool to perform the concept of sending early warning messages to target abnormal user(s) based on their gas usage among other features (see MPEP 2106.04(a)(2)(III)(B & C)).
Further, at the step directed in part to the ML model “trained” by “constructing a loss function” based on labels and initial prediction model outputs and iteratively updating the parameters of the “initial prediction model” until “the loss function satisfies a preset condition” to obtain the “trained prediction model” encompasses mathematical concepts that can be performed mentally or in pen and paper and requires specific mathematical calculations for the loss function construction. See MPEP 2106.04(a)(2)(I)(C).
Moreover, these same claim steps can fall under the abstract idea of a certain method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) in the form of “fundamental economic principles or practices” since the claimed steps, as discussed before, involve collecting a user gas usage/utility data to determine if the user has abnormal gas usage levels in the data by early flagging or warning the “smart gas service” providers and the targeted user via text or voice messages. Thus, by performing these claim functions the system is able to successfully avoid or mitigate economic risks for a business user when providing the gas product supplied and services provided to a user that might need to be prevented from “stealing” gas (see ¶0003 from Applicant disclosure).
At Step 2A Prong 2: For independent claims 1, 17 and 19, The judicial exception(s) or abstract idea previously identified is not integrated into a practical application (see MPEP 2106.04 (d)). The claims recite the additional element(s) of at least one server of a smart gas device management platform of Things (IoT) system; a gas device and a gas metering device (from claims 1 and 17); a smart gas indoor device parameter management sub-platform, a smart gas pipeline network device parameter management sub-platform, and a smart gas data center; a device operation parameter monitoring and warning module and a device parameter remote management module; and a prediction model that is a machine learning model, a terminal device (from claim 17); and non-transitory computer-readable storage medium (from claim 19). Also, these claims include the additional element in the steps for the “prediction model is trained by first training samples with first labels” being inputted “into an initial prediction model, constructing a loss function based on the first labels and an output of the initial prediction model, iteratively updating parameters of the initial prediction model based on the loss function until the loss function satisfies a preset condition” as well as the steps directed in part of “generating”, “transmitting” and “delivering” early warning messages for “controlling…a terminal device corresponding to the target abnormal user to push the early warning message to the target abnormal user in a text and/or voice manner”. These additional elements, individually and in combination, and while considering the claims as a whole, are merely used as a tool to perform the abstract idea (See MPEP 2106.05(f)). Specifically, these steps are recited as being performed by a computer that further uses a machine learning model (i.e. a prediction model) to determine abnormal users. The computer and ML model used are recited at a high level of generality that is being used as a tool to perform the generic computer functions for storing, monitoring, collecting and obtaining user and gas pipeline network transportation features data as clusters or groups to determine potential/target abnormal gas users to generate and transmit them early warning messages. Further, this ML model broadly claimed as well as the corresponding step for the ML model being “trained”, is not further limited to recite how the “determining” step is distinctively achieved from utilizing other general ML models or computer technology using ML models trained, even in view of ¶0105 and ¶0108 – 110 from Applicant disclosure which generally discusses the ML model and its training. Thus, these steps mentioned above are further describing and applying the abstract idea without placing any limits on how the technological components are being improved, while distinguishing in the claim language, the performing limitations from functions that generic computer components can perform.
Finally, the steps directed in part of “generating”, “transmitting” and “delivering” early warning messages for “controlling…a terminal device corresponding to the target abnormal user to push the early warning message to the target abnormal user in a text and/or voice manner” (i.e. interpreted as further sending instructions to transmit the text or voice message of the early warning) in the representative claim are really nothing more than links to computer for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components (refer to MPEP 2106.05 f (2)). Thus, in this limitation step, the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer.
Step 2B: For independent claims 1, 17 and 19, these claims do not provide an inventive concept. The recited additional elements of the claim(s) are the following: a smart gas device management platform of a smart gas Internet of Things (IoT) system and a gas device (from claims 1 and 17); and non-transitory computer-readable storage medium (from claim 19). These additional elements are not sufficient to amount significantly more than the judicial exception or abstract idea (see MPEP 2106.05). Because, as indicated in Step 2A Prong 2, these additional element(s) claimed are merely, instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Thus, even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept at Step 2B.
For dependent claims 2-16 and 18, the same analysis is incorporated. Due to their dependency to the independent claims analyzed, these claims cover or fall under the same abstract idea(s) of mental processes, mathematical concepts and certain method of organizing human activity. They describe additional limitations steps of:
Claims 2-16 and 18: further describes the abstract idea of the method for smart gas abnormal data analysis and how determining potential abnormal gas users includes: the generation of a plurality of histogram distributions to determine/count outlier users, different clustering parameters, frequency of times the user satisfies a number of preset conditions and outlier thresholds, weighting outlier degrees to determine abnormal probabilities, inputs including outlier user distribution map with nodes and edges, calculating reference correlation coefficients to further weight sub-difference correlations based on actual correlation coefficients and gas users’ clustering results, clustering parameters. Thus, being directed to the abstract idea group of mental processes as these steps require observations, evaluations and judgements. But also, these determining/calculating steps encompass mathematical concepts that can be performed mentally or in pen and paper and requires specific mathematical calculations. Finally, all these claims fall under certain method of organizing human activity for mitigating economic risks involving the monitoring of user’s gas usage data to prevent abnormal behaviors such as stealing or theft of the gas.
Step 2A Prong 2 and Step 2B: For dependent claims 7 – 8 and 18, these claims recite the additional elements of: a smart gas user platform, a smart gas service platform, a smart gas sensing network platform, and a smart gas object platform; the smart gas user platform is configured as a terminal device; smart gas object platform is configured for various types of indoor gas devices of a gas customer, various types of pipeline network devices and monitoring devices, the pipeline network device includes a gas gate station compressor, a gas flow meter, and a valve control device, and the monitoring device includes a temperature sensor and a pressure sensor; the smart gas sensing network platform is configured as a communication network and a gateway; a gas user sub-platform, a government user sub-platform, and a supervision user sub-platform; a smart gas use service sub-platform, a smart operation service sub-platform, and a smart supervision service sub-platform; a smart gas indoor device sensing network sub-platform and a smart gas pipeline network device sensing network sub-platform and a smart gas indoor device object sub-platform and a smart gas pipeline network device object sub-platform (from claim 18). These additional elements recited are invoking computers merely used as a tool to perform or “apply” the abstract idea(s) to the existing process of obtaining/transmitting gas user and gas pipeline network transportation features, data gas metering data as well as warning messages. Thus, amounting to no more than mere instructions to “apply” the exception using a generic computer component (MPEP 2106.05(f) and (f)(2)). See MPEP 2106.05(f). Accordingly, for the same reasons stated above, these additional element(s) claimed cannot provide an inventive concept at Step 2B.
Conclusion
The most pertinent and relevant prior art found, according to the latest search, for this reference was Siebel (U.S. Patent No. 11886843 B2) which teaches a computer systems and methods capable of “systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions” by determining the “signal values” for each signal and includes a machine learning (ML) model “applied to the signal values to identify energy usage conditions associated with non-technical loss” (see C1; L53 – 59; Siebel). Siebel teaches the step of “collecting” user and pipeline network transportation features from different meters and the “smart gas sensing network platform” being able to communicate through a “network and a gateway” with bidirectional transmission of the collected data and control instructions between platforms when determining “a set of signals and associated signal values for the set of signals” which are associated “with a plurality of energy usage conditions” wherein “a specific set of signal values can be associated with a current state of a specific utility meter for a specific customer at a specific location and venue” (see C19 – 20; L48 – 67 and L1 – 7; Fig. 1 and Fig. 4 (402); Siebel). The system from Siebel further teaches the “obtaining” of various clustering results as receiving the “new signals values for the set of signals” and a ML module with a ML algorithm facilitate the mapping of “some N-dimensional representations to non-technical loss” (see C21; L4 – 31; Fig. 4 (406); Fig. 6; Fig. 7 (706); Siebel). The step of “determine” potential abnormal gas users is also taught by Siebel when the Siebel system detects “data gaps and data anomalies (e.g., statistical anomalies), identify outliers, and conduct referential integrity checks” (see C11; L1 – 9; Fig. 3 (304); Siebel) as well as providing “signal values for the “Active Power vs. Reactive Power Curve Analysis” signal can characterize irregular variations in year-over-year consumption patterns for a given customer, which can indicate a likelihood of theft and/or malfunction” (see C16; L15 – 25; Siebel). Similarly, the claimed devices of gas usage and metering data is also taught by Siebel as the system imports data from many sources including meter data (see C8; L50 – 67; Siebel). For the limitation of a “prediction model is trained” with training samples inputs this is taught as “the classifier model” can receive “signal values” that are “indicative of normal energy usage” for determining “non-technical loss” (see C21; L54 – 67; Siebel). Finally, Siebel teaches the steps of ”generating” and “delivering” early warning messages as “stream processing logic” that can “provide a calculated result that can be persisted and used for subsequent analysis” and “provide an alert based on a calculated result” and/or “stream analytic services module 310” can send “streams of recently generated electricity consumption and demand data” into “an event queue associated with the data services module 306” that can trigger “automatic analytic processes” that can include outputs that may be “alerts and calculations” (see C12; L33 – 49 and C13; L4 – 15; Siebel). As for the “controlling” step is taught by Siebel in an example wherein “the stream processing logic also may provide an alert based on a calculated result. For example, a utility may want to receive alerts and on-the-fly analysis when there is an unexpected and significant drop or spike in load. This load variation could be caused by a malfunctioning piece of equipment or sudden damage to equipment, and could possibly represent great risk to the distribution system or an end customer. Data about the unexpected load change can be rapidly recognized, analyzed, and used to send the necessary alert” which is directed to early warning messages being quickly pushed. Further the messages in text format is satisfied since the “UI services module 324 ensures that end users enjoy a consistent visual experience, have access to capabilities and data relevant to their roles, and can interact with charts and reports delivering clear business insights” (see C12; L40 – 49: and C14; L35 – 41; Siebel).
However, Siebel did not taught the “abilities of specifically generating histogram distributions for the gas user clusters based on multiple preset gas use features to determine outlier users, counting gas users’ times in the cluster as an outlier user and determining a first abnormal user in the gas user cluster through a prediction model (i.e. ML model), training the prediction model by inputting first training samples with first labels to an initial prediction model, determining a specific target abnormal user based on a first and second abnormal user that are derived from their respective clustering results which were then covered or taught by Gas-Theft Suspect Detection Among Boiler Room Users: A Data-Driven Approach (referred to as Xiuwen hereafter by the Examiner). Xiuwen taught the step of “generating a plurality of histogram distributions” based on preset gas user features as shown in Figs. 3a – 3b histograms distributions for detecting “data-missing users” and “zero-consumption users” (see Section 3.1, ¶1 – 3, pp.4 – 5; Figs. 3 and 16; Xiuwen). The step of “determining” the outlier users with the histogram distributions to “count” the times each gas user is determined as an outlier is taught by Xiuwen as the analysis of Figs 3a – 3b wherein “the distribution of zero rate appears two obvious plunges, the first one after 10 percent and the second one after 70 percent. The high proportion of zero readings indicate that either longtime continuous or frequent irregular shutdown has occurred. It conflicts with normal operation patterns of boiler rooms and is highly suspicious of stealing gas” (see Section 3.1, ¶3, p.4 and ¶ 1 – 3, p. 5; Figs. 3 and 16; Xiuwen). Moreover, Xiuwen further teaches in Section 5, ¶1 – 2, p.18, more details of another histogram counting the suspicious users as shown in Fig. 16. The step of “determining” the abnormal user from the “gas user cluster through a prediction model” was also taught by Xiuwen as the application of a “One-Class Support Vector Machine” or “OCSVM based anomaly detection algorithm to capture multiple characteristic factors from different data sources” and distinguish “gas-theft suspects from irregular users” which can be “viewed as the pseudo label for designing a self-supervised or one class classification model” (see Section 3.3., ¶1, p.9 and Section 3.3.4., ¶1 - 2, p.12; Xiuwen). Finally, the step of “determining” target abnormal user based on the first and second abnormal users and their respective clustering results was taught by Xiuwen as the use of “deformation-based normality detection algorithm” to exclude “normal boiler rooms from abnormal ones” and for “distinguishing gas-theft suspects from irregular users, as shown in Fig. 11” a “OCSVM based anomaly detection algorithm” is used to “capture multiple characteristic factors from different data sources” (i.e. interpreted as the first and second clustering results per gas user) and “predict the detected abnormal users with the trained OCSVM to differentiate suspected users” based on three categories of features: “boiler room attribute features, gas consumption features, and temperature-gas joint features” (see Section 3.3, ¶1, p.9; Figs. 11 – 14; Xiuwen).
But neither Siebel or Xiuwen taught the abilities of having prediction model inputs that include an outlier user distribution map with nodes and edges that represent the gas users features and their environment and relationships based on distance and constructing a loss function that further is iteratively updating the parameters of the initial prediction model until the loss function satisfies a preset condition for the prediction model to be trained. Thus, A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction (referred to as Du hereafter by the Examiner) was satisfying these limitations. Firstly, Du teaches the “input of the prediction model” that include outlier user distribution map with nodes and edges representing gas user features, environment and relationships based on distance as the construction of a “knowledge-based spatial graph” with “nodes' features and topology information” as shown in Fig. 6 wherein “the pipeline network nodes are considered graph nodes, and the pipeline segments are considered edges. The node features of pipeline network nodes at each time state t can be processed as a feature matrix, where is the number of features for each node. In that way, the spatial dependency patterns that exist among different nodes can be represented as a directed graph, where represents the nodes and is the edges” (see C1; ¶1, p.7; Figs. 6 – 7; Du). Further, Du at least teaches the use of a “MSE loss function” to train the “proposed model” disclosed in Du. However, Du does not specifically details how such loss function is constructed to fully satisfy the step of “constructing a loss function” based on labels to “iteratively update” the parameters of an “initial prediction model” to train the prediction model, specifically based on the “loss function until the loss function satisfies a preset condition, so as to obtain the trained prediction model”.
Thus, none of the references disclosed above specifically, taught this step for “constructing” the “loss function” by iterating parameters for updates specifically based on “loss function satisfying a preset condition” to finalize the training of the prediction model claimed and neither taught the specific basis criteria for the step of “obtaining the first clustering result” by clustering the gas users, specifically, “gas consumption behavior similarity” and “physical pipeline connectivity” and the “complexity degree of a pipeline” and “same branch” origin included in the “clustering parameters” used.
Thus, the prior art cited principally failed in focusing on the specific step of “constructing” the loss function for the prediction model to be trained with updated parameters as well as the specific process of obtaining the clustering results with specific criteria and conditions. Finally, the prior art searched was not close enough to fully satisfy the disclosed claims of the invention, thus nothing else of record renders the missing elements as obvious. This analysis also extends to the steps of “sending instruction(s)” to obtain user and pipeline features by further “uploading” them to “a smart gas data center” via the “smart gas sensing network platform” claimed and “remotely set, adjust, and remotely authorize” these claimed “features” in the new claims 20 and 22.
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Clark (U.S. Patent No. 11656145 B2) is pertinent because it is about “methods, systems, and apparatus for monitoring consumable gas usage.”
Zhongyang (WO Pub No. 2024131236 A1) is pertinent because it “relates to a method, system, equipment and an Internet of Things cloud platform for predicting regional gas usage.”
Nottrott (U.S. Patent No. 10962437 B1) is pertinent because it “relates to systems and methods for detecting gas leaks such as methane leaks.”
Geuens (U.S. Pub No. 20220057048 A1) is pertinent because it is an “invention [that] is intended to be able to quantify leaks that occur in a gas network.”
Finkel (U.S. Pub No. 20170244726 A1) is pertinent because it “relates to a system and method for generating data compatible with an external system in an oil and gas asset supply chain, and in particular to an interface and interface method with a secure intermediary platform for generating secure and verifiable data to prevent tampering, or injection of unwanted data resulting from an unauthorized access along a supply chain.”
Ueki (U.S. Pub No. 20100330515 A1) is pertinent because it “relates to a gas shutoff device for controlling so as to limit use of a gas appliance at the CO occurring time using a CO alarm and a gas meter and in particular to a gas shutoff device for determining the CO gas leakage appliance according to an output signal from a CO alarm and securing safety.”
Silva (U.S. Pub No. 20230266195 A1) is pertinent because it “relates generally to gas leaks and, more particularly, to the quantification of gas leaks.”
Tao et. all, Intelligent Urban Sensing for Gas Leakage Risk Assessment (17 April 2023) is pertinent because it aims to “revolutionize the existing leakage detection system via the development of intelligent leakage risk assessment system for gas pipelines.”
Zheng, An online real-time estimation tool of leakage parameters for hazardous liquid pipelines (December 2020) is pertinent because it “establishes a pipeline digital twin model that simulates a pipeline leak to generate leakage data.”
Wu, Graph Neural Networks for Anomaly Detection in Industrial Internet of Things (02 July 2021) is pertinent because it “provides a useful investigation on graph neural networks (GNNs) for anomaly detection in IIoT-enabled smart transportation, smart energy, and smart factory.”
Zhang, Towards deep probabilistic graph neural network for natural gas leak detection and localization without labeled anomaly data (17 June 2023) is pertinent because it “proposes a deep probabilistic graph neural network in which attention-based graph neural network is built to model spatial sensor dependency.”
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ivonnemary Rivera Gonzalez whose telephone number is (571)272-6158. The examiner can normally be reached Mon - Fri 9:00AM - 5:30PM.
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626 /DENNIS W RUHL/Primary Examiner, Art Unit 3626
