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 .
This communication is in response to application 18/332,678 filed 06/09/2023, regarding applicant’s amendments and arguments/remarks filed 02/06/2026. Claims 1, 3, 5-7, 11, 13, 16, 18-19, are amended and hereby entered. Claims 2, 4, 15, and 17 are canceled. Claim 21 is added and hereby entered.
Response to Arguments
Applicant’s arguments regarding 35 USC 101 “Software per se” and 35 USC 103 are persuasive. However, Applicant’s arguments regarding 35 USC 101 claims directed to an abstract idea without practical application or significantly more are not persuasive. Additionally, upon further consideration, the grounds for rejection have been adjusted in view of applicant’s amended claim limitations.
Regarding 35 USC 101:
The applicant submits the claimed invention is not directed to a mental process because of the multiple platforms interacting with substantial amounts of data including correlation maps and computing devices not possible for humans to achieve mentally. However, the multiple platforms are used to supply data in which an analysis is performed (i.e. gas meter is used for the related data and information being transmitted to the computing device). Additionally, the applicant describes correlation maps as historical data sets, and the use of computers to perform data processing and analysis otherwise performed by a human recites an “apply it recitation”, wherein the computer is used as a tool to perform the abstract idea, see MPEP 2106.05(f). Further, the use of a physical aid to perform a mental step does not negate the mental nature of the limitation, but simply variations in memory capacity, see MPEP 2106.04(a)(2)(III)(B&C). This applies to a claim requiring a computer, in which the computer is used to perform the abstract idea.
The applicant submits the claimed invention integrates the judicial exception into practical application because of the technical features, “performing statement verification for a target handler based on the target statement parameter”, and “sending a short message service (SMS) reminder and/or a phone notification to the target handler of the gas work order based on a verification frequency and a verification intensity in the statement verification”. Specifically, the applicant proposes these features use information provided by the judicial exception to perform automated statement verification for the target handler. However, “performing statement verification for a target handler based on the target statement parameter” broadly recites performing data analysis for verification based on specific data. This recites data analysis at a high level, falling under the abstract idea of a mental process see MPEP 2106.04(a)(2)(III). Further, sending SMS reminders and notifying the handler via phone recites the presentation of analyzed data to the user via computer. Again, these elements are part of the abstract idea of mental processes (i.e. collecting information, analyzing it, and displaying certain results of the collection and analysis), not additional elements in which a technological improvement can be found. There is no improvement to the underlying computing technology or model technology. Rather the technology is used to perform the abstract idea, see MPEP 2106.05(f).
The applicant submits the training of the determination model using sample data, proximality vectors, and labeling, improves the functionality of the machine learning model. However, there is no technical improvement to the machine learning algorithm itself, and there is no algorithm recited. Rather, the claims recite training steps of inputting specific data types, labeling, and updating the model until criteria is met. These limitations do not improve the underlying technology of machine learning. Rather, they describe how a human interacts with a model being used to perform the abstract idea. Additionally, the amended features, analyzed both individually and as a whole, now recite new abstract ideas in addition to mental processes, (i.e. loss function reciting mathematical concepts) (i.e. steps for training a model and the claim as a whole reciting certain methods of organizing human activity), discussed further in the rejection below.
The applicant submits dependent claim 11 now recites technical features. However, these features again are not technical features for the same reasons already listed and are a part of the abstract idea.
The applicant submits the claims amount to significantly more because work order correlation maps and proximality vectors are now introduced as part of a technical solution to a technical problem. However, the proximality vectors are simply machine ingestible representations, work order information (i.e. data representations) and work order correlation maps are labeled relationships between data. Again, these limitations themselves are not considered additional elements that can amount to significantly more, rather they are part of the abstract idea of mental processes involving collecting information, analyzing it, and displaying the results of analysis. The additional elements recited in the claims comprise general-purpose computing components, gas devices, the determination model, and the neural network model. These elements fall under MPEP 2106.05(f) “apply it”, and do not amount to significantly more than the abstract idea. Therefore, the applicant’s arguments are unpersuasive, and the rejection is maintained.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) with no practical application and without significantly more.
The claimed invention is directed to an abstract idea in that the instant application is directed to a certain methods of organizing human activity, mental processes, and mathematical concepts (See MPEP 2106.04(a)(2)(I-III)). The independent claims recite:
Claims 1 and 13 (substantially similar in scope and language): “A method for platform intelligent statement based on operation of smart gas, implemented by a smart gas management platform of an Internet of Things (loT) system for platform intelligent statement based on operation of smart gas, the IoT system further comprising a smart gas user platform, a smart gas service platform, a smart gas sensor network platform, and a smart gas object platform, wherein the smart gas user platform includes a terminal device, the smart gas service platform includes a processing device, the smart gas management platform includes the processing device and a storage device, the smart gas object platform includes a gas indoor device and gas pipeline network device, the terminal device includes a mobile device or a tablet computer, the processing device includes a server or a server group, the storage device includes a database, the gas indoor device includes a gas meter and an indoor gas pipeline, the gas pipeline network device includes an outdoor gas pipeline, a valve control device, a gas storage tank, and a pressure regulation device; the smart gas service platform is configured to obtain information related to a gas work order from the smart gas management platform and send the information related to the gas work order to the smart gas user platform; the smart gas object platform is configured to monitor and generate operation information of the gas indoor device and the gas pipeline network device, and upload the operation information to the smart gas management platform through the smart gas sensor network platform; and the method comprising: obtaining work order information of a target gas work order, the work order information including a gas user type, a work order type, a work order urgency, and work order correlation information; determining a statement demand degree of the target gas work order based on the work order information, wherein the statement demand degree refers to a demand degree for timeliness of a statement and accuracy of statement data, the statement characterizes a state that the target gas work order has been processed and confirmed to be completed, and the determining a statement demand degree of the target gas work order based on the work order information includes: determining a statement influenced object based on the work order information, the statement influenced object including an influenced gas work order or an influenced gas user; determining a correlation influenced degree based on the statement influenced object, wherein the correlation influence degree includes a gas work order correlation influence degree and a gas user correlation influence degree, and the determining a correlation influenced degree based on the statement influenced object includes: constructing a work order correlation map based on the target gas work order and the statement influenced object, a node of the work order correlation map including a target gas work order node, an influenced gas work order node, or an influenced gas user node; an edge of the work order correlation map being used to connect the target gas work order node with the influenced gas work order node or the influenced gas user node; a feature of the node including a node category and a distance between each node and the target gas work order node; a feature of the edge including a degree of influence, and the degree of influence being related to an influence duration of gas usage of the influenced gas user, gas usage of the influenced gas user, an interval between required completion times and work order urgencies of adjacent influenced gas work orders; and inputting the work order correlation map and proximality vector into a correlation influence degree determination model and outputting the gas work order correlation influence degree of the gas user correlation influence degree based on processing of the correlation influence degree determination model, the proximality vector including a proximality value between each influenced gas work order node of influenced gas user node and the target gas work order node in the work order correlation map, and the correlation influence degree determination model being a convolutional neural network model; wherein the correlation influence degree determination model is obtained by training a plurality of labeled sample work order correlation maps and sample proximality vectors of the plurality of labeled sample work order correlation maps, wherein each of the plurality of sample work order correlation maps is a historical work order correlation map, the sample proximality vector is obtained base on each of the plurality of sample work order correlation maps, and a label is determined based on the gas work order correlation influence degree of the gas user correlation influence degree of the target gas work order node in each of the plurality of sample work order correlation maps; wherein the training of the correlation influence degree determination model includes: inputting each of the plurality of sample work order correlation maps and a corresponding sample proximality vector into an initial correlation influence degree determination model; constructing … based on the label of each of the plurality of sample work order correlation maps and an output of the initial correlation influence degree determination model; and iteratively updating a parameter of the initial correlation influence degree determination model based on the … until a preset condition is satisfied and completing the training to obtain a trained correlation influence degree determination model; and determining the statement demand degree of the target gas work order based on the correlation influence degree; determining a target statement parameter based on the statement demand degree, the target statement parameter including a statement mode, a statement time limit, and a statement verification parameter; and performing a statement verification for a target handler based on the target statement parameter, wherein the statement verification is used to indicate an operation that checks a statement status of the target handler, the target handler refers to a handler corresponding to the targe gas work order, and the performing statement verification for a target handler based on the target statement parameter includes: sending a Short Message Service (SMS) reminder and/or a phone notification to the target handler of the target gas work order based on a verification frequency and verification intensity in the statement verification parameter”.
These claim limitations, (that is the non bolded claim language), are being interpreted as commercial interactions (including advertising, marketing or sales activities or behaviors, or business relations) and managing personal behavior or relationships or interactions between people (including following rules and instructions) including certain activities between a person and computer. Training a model to process work order data for prioritization and actionable insights recites a commonplace business method involving business relations and managing interactions (including interactions between person and computer). Further, the use of the prioritized information to manage target handlers recite managing behavior by following rules or instructions. The claims recite an abstract idea consistent with the “certain methods of organizing human activity” grouping set forth in the MPEP 2106.04(a)(2)(II).
Additionally, the claimed invention is directed to an abstract idea in that the instant application is directed to mental processes, (See MPEP 2106.04(a)(2)(III)). These claim limitations, (that is the non bolded claim elements), are being interpreted as concepts performed in the human mind (including observation, evaluation, judgement, and opinion). The independent claims recite evaluating data associated with pipelines and work orders to perform verification based on the processed data. Using work order data to determine demand and parameters to further perform verification based on the determination can equivalently be achieved by the human mind. For example, analyzing the work order information to determine correlations between work orders and verify appropriate timelines of repairs can be performed in the human mind. The claims recite an abstract idea consistent with the “mental process” grouping set forth in the MPEP 2106.04(a)(2)(III).
Additionally, the claimed invention is directed to an abstract idea in that the instant application is directed to mathematical concepts, (See MPEP 2106.04(a)(2)(I)). The claim limitation, “a loss function”, is interpreted as a mathematical formulas or equations. Constructing a loss function involves mathematical calculations and formulas consistent with the “mathematical concepts grouping set forth in the MPEP 2106.04(a)(2)(I).
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites an “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea. The instant application is directed towards a method and systems to implement the identified abstract ideas of certain methods of organizing human activity, mental processes, and mathematical concepts in a general computer environment. The claims do not include additional elements that integrate the judicial exception into practical application or amount to significantly more than the judicial exception. The independent claims recite the additional elements “a terminal device”, “a processing device”, “a storage device”, “a gas indoor device and gas pipeline network device, the terminal device includes a mobile device or a tablet computer, the processing device includes a server or a server group, the storage device includes a database, the gas indoor device includes a gas meter and an indoor gas pipeline, the gas pipeline network device includes an outdoor gas pipeline, a valve control device, a gas storage tank, and a pressure regulation device;”, “a terminal device”, “a processing device”, “a storage device”, and “a gas indoor device and gas pipeline network device, the terminal device includes a mobile device or a tablet computer, the processing device includes a server or a server group, the storage device includes a database, the gas indoor device includes a gas meter and an indoor gas pipeline, the gas pipeline network device includes an outdoor gas pipeline, a valve control device, a gas storage tank, and a pressure regulation device”. These claim elements are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a general computer environment. The machines merely act as a modality to implement the abstract idea and are not indicative of integration into a practical application (i.e., the additional elements are simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed in Step 2A Prong Two analysis, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in 2B and does not provide an inventive concept.
Regarding the dependent claims:
Claims 11-12 and 19 recite a risk value, “determining an overtime risk value based on an actual time difference between the required completion time and a current time”. This limitation is being interpreted as a mathematical concepts in which a mathematical relationship and calculation is recited, see MPEP 2106.04(a)(2)(I). Determining a risk value based on time difference recites a numeral mathematical relationship to calculate risk. Further, the additional elements do no more than mere instructions to apply the exception using a general computer environment. The machines merely act as a modality to implement the abstract idea and are not indicative of integration into a practical application or significantly more (i.e., the additional elements are simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
Claim 20 recites a new additional element, “a non-transitory computer-readable storage medium” and “a computer”. Although the claim recites these additional elements, the instant application fails to integrate the judicial exception into a practical application because the instant application merely recites an “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea. The instant application is directed towards the previously identified abstract ideas. The general-purpose computer components merely act as a modality to implement the abstract idea and are not indicative of integration into a practical application or significantly more (i.e., the additional elements are simply used as tools to perform the abstract idea), see MPEP 2106.05(f).
Claims 3, 5-10, 14, 16, 18, and 21 recite further embellishments of the existing abstract ideas of certain methods of organizing human activity and mental processes. The claims limit the abstract ideas by incorporating work order map details and prioritization based on different determinations, and they limit management of handlers. They recite no new additional elements and no new abstract ideas for consideration under 35 USC 101.
Subject Matter Allowable Over Prior Art
With respect to prior art, independent claims 1 and 13 are allowable over prior art. However, the instant invention is not allowable due to the deficiencies outlined above regarding 35 USC 101.
The instant invention claims a method for operation of a smart gas management platform in which gas pipeline work orders are prioritized. Prior art Tominaga (US 20200401971 A1) teaches a determining priority levels of maintenance on pipelines. See at least Tominaga paragraph 9 stating, “an asset management device according to the present invention includes an input means, an evaluation item setting means, a priority level calculation means, and an output means. The input means receives an input of information on a pipe being a diagnostic target as pipe information. The evaluation item setting means sets an evaluation item, based on the pipe information that is commonly shared by a plurality of pipes being diagnostic targets. The priority level calculation means 3 calculates a priority level of a response for each of the pipes being diagnostic targets with respect to the evaluation item. The output means outputs data for comparing calculated priority levels among the pipes being diagnostic targets”. Tominaga further teaches structural elements and method steps regarding the processing of gas and work order information with influence considerations. See at least Tominaga paragraph 34 stating, “The various pieces of information on the piping installation may further include information on elements that influence pressure vibration of pipes, such as a tank and a valve”, and paragraph 35 stating, “The various types of information on the piping installation used for a network of water pipes may include an amount of water consumption of a consumer who consumes water from the network of water pipes, a water reservoir connected to the network of water pipes, a tank similarly connected to the network of water pipes, a pressure regulating valve, presence or absence of important installations, environmental influence information, consumer complaints information, and the like. The presence or absence of important installations indicates presence or absence of facilities such as a hospital and a factory near a target pipe where suspension of water supply is required to be avoided particularly”. However, Tominaga is silent regarding a correlation map with nodes and edges, training a model for correlation influence with sampled correlation maps, constructing loss functions, and handler notifications.
Prior art Sahni (US 20160103433 A1) teaches correlation maps with nodes and edges regarding pipeline maintenance and repair urgencies. See at least Sahni Figures 4-8 and paragraph 27 stating, “The pipelines 410, 420 include actual pipe segments along with other pipeline assets. Note that risk information about various segments of the pipelines 410, 420 may also be included on the display 400 (e.g., low risk segments may be displayed as green or high risk segments may be displayed with crosshatching as illustrated in FIG. 4)”, (Para 0034) “A work order area 1210 may provide, for example, a pipeline name or identifier, a work order number or identifier, a description of an anomaly, a location, a description of an asset, a priority lever, a start date indicating when the work order was opened, an end date when the risk was addressed”. Further, Sahni teaches other structural elements. See at least Sahni Paragraph 17 stating, “According to some embodiments, the data sources 110 include information about subsystem assets, such as pipeline compressor stations, main line valves, meters”. However, Sahni is silent regarding training a model for correlation influence with sampled correlation maps, constructing loss functions, and handler notifications.
Prior art Milosevic (US 20210167986 A1) teaches a training a model with loss functions, for emergency hazard detection regarding utility meters. See at least Milosevic paragraph 52 stating, “Another algorithm may be used which is a machine learning algorithm for detection of a potential hazardous situation which may be a recurrent neural network (e.g., 2-layer LSTM, with 32 units per layer) and a quantile regression loss function may be applied, and the model may be trained by an Adam algorithm, using truncated backpropagation on randomly drawn 2-week segments of data”. However, the art is unrelated to work order prioritization and the model does not determine correlation influence degree of work orders. Rather, the art teaches a different determination model trained differently for a different purpose and is silent regarding correlation maps, handler notifications and work order prioritization.
Prior art Mitchell (US 20100312605 A1) teaches handler alerts and processing times for work orders. See at least Mitchell paragraph 146 stating, “For example, FIG. 10 illustrates a set of alert trigger rules 1000. As illustrated by rules 1010-1020, a schedule or quality issue may be detected if progress of a technician falls a certain percentage (e.g., twenty five percent) behind expected progress or if a work order is not completed after a threshold amount of time (e.g., fifty minutes, thirty minutes, etc.) has elapsed”. However, the art is silent regarding gas work order maps, loss functions, and gas work order prioritization. Rather, the art teaches management of handlers or technicians.
Prior art Tapia (US 20180268258 A1) teaches a generic decision making machine learning model that uses correlation maps. See at least Tapia paragraph 72 stating, “The training window 1100 also includes a correlation map 1104 that maps all samples. A selected sample 1106 that has been selected by a user is shown in the correlation map 1104 together with samples 1108 that are similar to the selected sample 1106”. However, the model teaches a training method unrelated to a correlation influence model, and the art is silent on work order prioritization, influence models, loss functions, and handler notifications.
None of the references alone or in combination fully teach training the correlation determination model with sample work order correlation maps and labeling based on correlation influence degrees of work orders and users. Additionally, not only does the combination not fully teach the claim limitations, one of ordinary skill would not have been motivated to combine the prior art presented. Therefore, claim 1 contains subject matter allowable under prior art, but the application is not allowable due to the deficiencies regarding 35 USC 101.
Conclusion
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 Examiner Benjamin Truong, whose telephone number is 703-756-5883. The examiner can normally be reached on Monday-Friday from 9 am to 5 pm (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber SPE can be reached on 571-270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300 Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/B.L.T. /Examiner, Art Unit 3626
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
