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
This is a final. Claims 1-7, 9-20 are pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 10/23/2025 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Continuation
This application is a continuation of U.S. application 18306215 (filed 04/24/2023). See MPEP §201.08. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Applications. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Applications are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
Terminal Disclaimer
The terminal disclaimer filed on 15 June 2026 disclaiming the terminal portion
of any patent granted on this application which would extend beyond the expiration
date of Application No. 18/306,215 (filed , now U.S. Patent No. 12,423,632) has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Amendment
The previously pending rejection under 35 USC 101, will be maintained. The 101 rejection is updated in light of the new claims.
With regard to the rejection under 35 USC 102/103- with respect to the art rejection have been fully considered and are persuasive, the rejection under 35 USC 102/103 has been withdrawn. No art rejection has been put forth in the rejection for the reason found in the “Allowable Subject Matter” section found below.
Response to Arguments
Applicant's arguments filed 06/15/2026 have been fully considered but they are not persuasive, moreover, any new grounds of rejection have been necessitated by applicant’s amendments to the claims,
Response to Arguments under 35 USC 101:
Applicant argues (Pages 13-14 of the remarks): with regard to Step 2A, Prong One
In particular, when compared to Example 39 of Subject Matter Eligibility Examples: Abstract Ideas issued on January 7, 2019, which also recites a method for training a neural network, the same analysis precisely applies to amended claim 1 because the limitations of amended claim 1 are not practically performed in the human mind. Similar to claim 1 of Example 39, the current amended claim 1 does not recite a mental process.
Thus, amended independent claim 1 is eligible because it does not recite a judicial exception.
Examiner respectfully disagrees:
The Applicant's Specification titled " METHODS AND INTERNET OF THINGS (IOT) SYSTEMS FOR MAINTENANCE MANAGEMENT OF SMART GAS CALL CENTER" emphasizes the need for data analysis, "In summary, the present disclosure relates to methods and systems for adjusting work order based on call consultation data, location data and maintenance evaluation value. In example aspects, based on received data " (Spec. figure 2).
Applicant's claims as recited above provide a business solution of adjusting work order based on call consultation data, location data and maintenance evaluation value. Applicant's claimed invention pertains to commercial/legal interactions because the limitations recite adjusting work order based on call consultation data, location data and maintenance evaluation value. which pertain to "agreements in form of contract, legal obligations; advertising, marketing or sales activities or behaviors and business relations" expressly categorized under commercial/legal interactions. See MPEP §2106.04(a)(2)(II).
Furthermore, As the bolded claim limitations above demonstrate, independent claims 1, and 15 recites the abstract idea of generating location evaluation value, maintenance evaluation value based on the call consultation data. which is “observation, evaluations, judgments, and opinions,” expressly categorized under mental processes. See MPEP §2106.04(a)(2)(II).
Applicant argues (Pages 14-23 of the remarks): with regard to Step 2A, Prong Two and 2B
Amended claim 1 provides that location accuracy is positively correlated with the weight of maintenance feature vectors, and that the deviation between the fault type output by the fault prediction model and the actual on-site fault type is used to reversely adjust the multi-dimensional scoring weights of maintenance personnel. Meanwhile, the complexity dimensions of the operation and maintenance are defined using both pipeline network complexity (gas source density, number of pipeline branches, total length of transmission/distribution pipelines) and historical fault distribution. This set of evaluation rules is specifically designed for capability grading of gas pipeline network maintenance personnel and precise work order matching. Amended claim 1 is not a conventional mental process for general enterprise personnel evaluation or work order dispatch, falls outside the scope of abstract business activities, and constitutes a specific improvement to gas loT operation and maintenance dispatching technology.
Furthermore, amended claim 1 relies on a dedicated gas sensor array, a …
loT architecture, a customized machine learning fault model, and multidimensional
physical operating parameters to solve the specific technical drawbacks in
the gas industry caused by traditional manual dispatch - such as capability mismatch,
inaccurate fault localization, and high user complaint rates. Amended claim 1 achieves
substantial technical improvements in fault prediction efficiency, work order matching
accuracy, and pipeline network operation safety for the gas loT system, which applies
the abstract ideas to practical application.
…
Independent amended claim 15 recites features similar to those of amended claim 1, and thus, contains the same limitations that qualify as "Significantly More" for at least the reasons set forth above with respect to amended claim 1 and for the additional features cited therein.
Claims 2-7, 9-14, and 16-20 depend from claims 1 and 15, respectively, and thus, contain the same limitations that qualify as "Significantly More" for at least the reasons set forth above with respect to amended claims 1 and 15 and for the additional features cited therein.
Examiner respectfully disagrees:
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional element, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use exception, such that it is more than a drafting effort designed to monopolize the exception.
The claims recites the additional limitation an Internet of Things (loT) system, user platform, service platform, management platform, sensor network platform, a terminal device, a communication network and gateway, device object sub-platform, sub-platform, the gas indoor device object sub-platform includes a metering device of a gas user, the gas pipeline network device object sub-platform includes at least one of a pressure regulation device, a gas gate station compressor, prediction model, and a machine learning model are recited in a high level of generality and recited as performing generic computer functions routinely used in computer applications. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp. 134 S. Ct, at 2360,110 USPQ2d at 1984 (see MPEP 2106.05(f). All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general purpose computer.
The additional elements of a “model and machine learning model”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “model and machine learning model” is insufficient to show a practical application of the recited abstract idea.
The use of generic computer component does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (step 2A-prong two: NO).
Further, with regard to mining (i.e., searching over a network), receiving, processing, storing data, and parsing (i.e. extract, transform data), the courts have recognized the following computer functions as well-understood, routing, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (i.e. “receiving, processing, transmitting, storing data”, etc.) are well-understood, routine, etc. (MPEP 2106.05(d))
The Alice framework, step 2B (Part 2 of Mayo) determine if the claim is sufficient to ensure that the claim amounts to “significantly more” than the abstract idea itself. These additional elements recite conventional computer components and conventional functions of:
Claims 1, and 15 does not include my limitations amounting to significantly more than the abstract idea, along. Claims 1, and 15 includes various elements that are not directed to the abstract idea. These elements include “an Internet of Things (loT) system, user platform, service platform, management platform, sensor network platform, a terminal device, a communication network and gateway, device object sub-platform, sub-platform, the gas indoor device object sub-platform includes a metering device of a gas user, the gas pipeline network device object sub-platform includes at least one of a pressure regulation device, a gas gate station compressor, prediction model, and a machine learning model”
Examiner asserts that an Internet of Things (loT) system, user platform, service platform, management platform, sensor network platform, a terminal device, a communication network and gateway, device object sub-platform, sub-platform, the gas indoor device object sub-platform includes a metering device of a gas user, the gas pipeline network device object sub-platform includes at least one of a pressure regulation device, a gas gate station compressor, prediction model, and a machine learning model are a generic computing element performing generic computing functions. (See MPEP 2106.05(f))
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices.
In addition, fig. 1, of the specifications detail any combination of a generic computer system program to perform the method. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The computing elements with a computing device is recited at high level of generality (e.g. a generic device performing a generic computer function of processing data). Thus, this step is no more than mere instructions to apply the exception on a generic computer. In addition, using a processor to process data has been well-understood routing, conventional activity in the industry for many years.
Generic computer features, such as system or storage, do not amount to significantly more than the abstract idea. These limitations merely describe implementation for the invention using elements of a general-purpose system, which is not sufficient to amount to significantly more. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am. Inc., 793 F .3d 1306, 1334, 115 USPQ2d 1681, 1791 (Federal Circuit 2015).
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 1-7, 9-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without a practical application or significantly more than the abstract idea.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Regarding Step 1
Claims 1-7, and 9-14 are directed toward a system (machine). Claims 15-20 are directed to a method (process). Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1]
Claims 1-7, and 9-20 are directed toward the judicial exception of an abstract idea.
Independent claim 15 recites essentially the same abstract features as claim 1, thus are abstract for the same reason as claim 1.
Regarding independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1. An Internet of Things (loT) system for allocating maintenance tasks based on smart gas, comprising a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform which interact in sequence, wherein:
the smart gas user platform is configured as a terminal device;
the smart gas sensor network platform is configured as a communication network and gateway;
the smart gas object platform includes a gas indoor device object sub-platform and a gas pipeline network device object sub-platform, the gas indoor device object sub-platform includes a metering device of a gas user, the gas pipeline network device object sub-platform includes at least one of a pressure regulation device, a gas gate station compressor, a gas flow meter, a valve control device, a thermometer, and a barometer; and
the smart gas management platform is configured to:
obtain call consultation data information of the gas user through the smart gas service platform based on the smart gas user platform;
generate first data information from the call consultation data information, the first data information including relevant data information generated by maintenance of a maintainer to be evaluated within a target time period;
generate one or more maintenance feature vectors of the maintainer to be evaluated based on the first data information, each of the one or more maintenance feature vectors corresponding to one maintenance;
determine a first maintenance evaluation value for the one or more maintenance feature vectors;
generate second data information from the call consultation data information, the second data information including relevant data information generated by a fault location of an agent to be evaluated within the target time period;
determine, based on the second data information, user-side gas feature data, gas composition features, gas entry features, and gas upstream transportation features;
wherein the gas composition features include a gas composition; the gas entry features include pressures, flow velocities, and temperatures of gas at a plurality of points in a gas entry section; and
the gas upstream transportation features include pressures, flow velocities, and temperatures of gas at a plurality of points in an upstream gas transportation section;
input the user-side gas feature data, the gas composition features, the gas entry features, and the gas upstream transportation features into a gas fault prediction model to predict a gas fault type, the gas fault prediction model being a machine learning model;
generate a location accuracy of one or more location feature vectors based on the gas fault type and an actual gas fault type, the one or more location feature vectors corresponding to the one or more maintenance feature vectors, for each of a plurality of dimensions,
determine a weight for the one or more maintenance feature vectors in the dimension based on the location accuracy of the one or more location feature vectors corresponding to the one or more maintenance feature vectors in the dimension; and
determine a maintenance assessment value for the dimension based on the first maintenance assessment value of the one or more maintenance feature vectors in the dimension and the weight for the one or more maintenance feature vectors;
adjust a work order processing scope of the maintainer to be evaluated based on a multi-dimensional maintenance evaluation value; and
instruct a smart operation management sub-platform to allocate subsequent maintenance tasks based on an adjusted work order processing scope of the maintainer to be evaluated;
wherein the gas fault prediction model is obtained by training based on a plurality of training samples with labels, the plurality of training samples include the user-side gas feature data, the gas composition features, the gas entry features, and the gas upstream transportation features of historical gas users, and the labels include known gas fault types of the historical gas users, and the smart gas management platform is further configured to:
input the plurality of training samples into an initial gas fault prediction model, and construct a loss function through the labels and results of the initial gas fault prediction model;
iteratively update parameters of the initial gas fault prediction model through gradient descent based on the loss function; and
when a preset condition is met, complete model training and obtain a trained gas fault prediction model, wherein the preset condition includes the loss function converges or a count of iterations reaches a threshold.
The Applicant's Specification titled " METHODS AND INTERNET OF THINGS (IOT) SYSTEMS FOR MAINTENANCE MANAGEMENT OF SMART GAS CALL CENTER" emphasizes the need for data analysis, "In summary, the present disclosure relates to methods and systems for adjusting work order based on call consultation data, location data and maintenance evaluation value. In example aspects, based on received data " (Spec. figure 2).
Applicant's claims as recited above provide a business solution of adjusting work order based on call consultation data, location data and maintenance evaluation value. Applicant's claimed invention pertains to commercial/legal interactions because the limitations recite adjusting work order based on call consultation data, location data and maintenance evaluation value. which pertain to "agreements in form of contract, legal obligations; advertising, marketing or sales activities or behaviors and business relations" expressly categorized under commercial/legal interactions. See MPEP §2106.04(a)(2)(II).
Furthermore, As the bolded claim limitations above demonstrate, independent claims 1, and 15 recites the abstract idea of generating location evaluation value, maintenance evaluation value based on the call consultation data. which is “observation, evaluations, judgments, and opinions,” expressly categorized under mental processes. See MPEP §2106.04(a)(2)(II).
Dependent claims 2-7, 9-14, and 16-20 further reiterate the same abstract ideas with further embellishments, such as
claim 2 wherein the smart gas management platform includes a smart customer service management sub-platform, the smart operation management sub-platform, and a smart gas data center, the smart customer service management sub- platform bidirectionally interacts with the smart gas data center, the smart operation management sub-platform bidirectionally interacts with the smart gas data center, and the smart customer service management sub-platform and the smart operation management sub-platform obtain data from the smart gas data center and feedback corresponding operation information; the smart gas user platform includes a gas user sub-platform, a government user sub-platform, and a supervision user sub-platform, the gas user sub-platform corresponds to the gas user, the government user sub-platform corresponds to a government user, and the supervision user sub-platform corresponds to a supervision user; and the smart gas service platform includes a smart gas usage service sub-platform, a smart operation service sub-platform, and a smart supervision service sub-platform, the smart gas usage service sub-platform corresponds to the gas user sub-platform, the smart operation service sub-platform corresponds to the government user sub-platform, and the smart supervision service sub-platform corresponds to the supervision user sub- platform.
claim 3 (Similarly claim 16) wherein the first data information includes one or more of a maintenance complexity of each time of gas fault maintenance among all times of gas fault maintenance within the target time period, a total time spent on maintenance, a count of maintenance trips, a customer complaint situation, and a time interval between a maintenance time and a complaint time, the maintenance complexity including a simple maintenance task, an intermediately difficult maintenance task, and a complex maintenance task.
claims 4 (Similarly claim 17) wherein the smart gas management platform is further configured to: determine a maximum maintenance evaluation value based on the multi- dimensional maintenance evaluation value; and if a current work order processing range corresponding to the maintainer to be evaluated is not within a dimension corresponding to the maximum maintenance evaluation value, adjust the current work order processing range corresponding to the maintainer to be evaluated to be within the dimension corresponding to the maximum maintenance evaluation value.
claims 5 (Similarly claim 18) wherein the weight is positively correlated with the location accuracy of the one or more location feature vectors.
claims 6 (Similarly claim 19) wherein each of the plurality of dimensions corresponds to a maintenance complexity, and a dimension of each of the one or more maintenance feature vectors is determined based on the maintenance complexity corresponding to the maintenance feature vector; the maintenance complexity is generated based on a gas fault distribution of maintenance corresponding to the maintenance feature vector and a gas pipeline network complexity, and the gas fault distribution includes a count of different types of gas faults in historical gas faults of the gas user corresponding to the maintenance feature vector.
claim 7 wherein the smart gas management platform is further configured to: determine the gas pipeline network complexity based on a gas source distribution density, a count of pipeline branches, and a total length of transmission and distribution pipelines of a pipeline network of the gas user and an upstream transmission and distribution pipeline network.
claim 8 Cancelled
claim 9 wherein the call consultation data information further includes first associated data, and the first associated data includes at least gas usage feature information of the gas user.
claim 10 wherein the smart gas management platform is further configured to: generate a multi-dimensional location evaluation value of the agent to be evaluated in the plurality of dimensions based on the second data information.
claim 11 wherein the smart gas management platform is further configured to: generate one or more location feature vectors of the agent to be evaluated based on the second data information; and generate the multi-dimensional location evaluation value based on the one or more location feature vectors.
claim 12 wherein each of the plurality of dimensions corresponds to a location complexity, each of the one or more location feature vectors corresponds to a fault location, and the smart gas management platform is further configured to: calculate a first location evaluation value for each location feature vector in each dimension based on the one or more location feature vectors; and perform a weighted summation on a plurality of first location evaluation values obtained by calculating the one or more location feature vectors in the each dimension to generate the multi-dimensional location evaluation value, a weight of the weighted summation being related to a corresponding location accuracy, a dimension of the maintenance feature vector being determined based on a location complexity corresponding to the fault location, and the location complexity corresponding to the fault location being generated based on a model output ambiguity corresponding to the each location feature vector and a gas fault distribution.
claim 13 wherein the smart gas management platform is further configured to: input the user-side gas feature data, the gas composition features, the gas entry features, and the gas upstream transportation features, and first associated data into the gas fault prediction model to determine the gas fault type.
claim 14 wherein the location accuracy is also related to a model output ambiguity corresponding to the each location feature vector, and the dimensions of the one or more location feature vectors are generated based on clustering, and elements in clustering feature vectors include the model output ambiguity, a gas fault distribution, the user-side gas feature data, and a gas user type.
claim 20 A non-transitory computer-readable storage medium storing computer instructions, wherein when the computer instructions are executed by a processor, the method of claim 15 is implemented.
which are nonetheless directed towards fundamentally the same abstract ideas as indicated for independent claims 1, and 15.
Regarding Step 2A [prong 2]
Claims 1-7, 9-20 fail to integrate the abstract idea into a practical application. Independent claims 1, and 15 include the following additional elements which do not amount to a practical application:
Claim 1. An Internet of Things (loT) system … user platform, .. service platform, .. management platform, .. sensor network platform, .. platform which interact in .. a terminal device;
.. a communication network and gateway;
… device object sub-platform .. sub-platform, the gas indoor device object sub-platform includes a metering device of a gas user, the gas pipeline network device object sub-platform includes at least one of a pressure regulation device, a gas gate station compressor, .. device, … platform is configured to:
…
prediction model to predict a gas fault type, the gas fault prediction model being a machine learning model;
.. sub-platform
Claim 16. An Internet of Things (loT) system … user platform, .. service platform, .. management platform, .. sensor network platform, .. platform which interact in .. a terminal device;
.. a communication network and gateway;
… device object sub-platform .. sub-platform, the gas indoor device object sub-platform includes a metering device of a gas user, the gas pipeline network device object sub-platform includes at least one of a pressure regulation device, a gas gate station compressor, .. device, … platform is configured to:
…
prediction model to predict a gas fault type, the gas fault prediction model being a machine learning model;
.. sub-platform
The bolded limitations recited above in independent claims 1, and 15 pertain to additional elements which merely provide an abstract-idea-based-solution implemented with computer hardware and software components, including the additional elements of an Internet of Things (loT) system, user platform, service platform, management platform, sensor network platform, a terminal device, a communication network and gateway, device object sub-platform, sub-platform, the gas indoor device object sub-platform includes a metering device of a gas user, the gas pipeline network device object sub-platform includes at least one of a pressure regulation device, a gas gate station compressor, prediction model, and a machine learning model which fail to integrate the abstract idea into a practical application because there are (1) no actual improvements to the functioning of a computer, (2) nor to any other technology or technical field, (3) nor do the claims apply the judicial exception with, or by use of, a particular machine, (4) nor do the claims provide a transformation or reduction of a particular article to a different state or thing, (5) nor provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of MPEP §2106.04(d)(1) and §2106.05 (a-c & e-h), (6) nor do the claims apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, in view of MPEP §2106.04(d)(2). The Specification provides a high level of generality regarding the additional elements claimed without sufficient detail or specific implementation structure so as to limit the abstract idea, for instance, " The methods may also be at least partially embodied in the form of a computer into which computer program code is loaded or executed, such that, the computer becomes a special purpose computer for practicing the methods. When implemented on a general-purpose processor, the computer program code segments configure the processor to create specific logic circuits. The methods may alternatively be at least partially embodied in application specific integrated circuits for performing the methods. (Spec. fig. 1). Nothing in the Specification describes the specific operations recited in claim 1 (Similarly claim 15) as particularly invoking any inventive programming, or requiring any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is somehow implemented using any specialized element other than all-purpose computer components to perform recited computer functions. The claimed invention is merely directed to utilizing computer technology as a tool for solving a business problem of data analytics. Nowhere in the Specification does the Applicant emphasize additional hardware and/or software elements which provide an actual improvement in computer functionality, or to a technology or technical field, other than using these elements as a computational tool to automate and perform the abstract idea. See MPEP §2106.05(a & e).
The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant's claimed invention which merely pertains to steps for adjusting work order based on call consultation data, location data and maintenance evaluation value and the additional computer elements a tool to perform the abstract idea, and merely linking the use of the abstract idea to a particular technological environment. See MPEP §2106.04 and §21062106.05(f-h). Alternatively, the Office has long considered data gathering, analysis and data output to be insignificant extra-solution activity, and these additional elements do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.04 and §2106.05(g). Thus, the additional elements recited above fail to provide an actual improvement in computer functionality, or to a technology or technical field. See MPEP §2106.04(d)(1) and §2106§2106.05 (a & e).
Instead, the recited additional elements above, merely limit the invention to a technological environment in which the abstract concept identified above is implemented utilizing the computational tools provided by the additional elements to automate and perform the abstract idea, which is insufficient to provide a practical application since the additional elements do no more than generally link the use of the abstract idea to a particular technological environment. See MPEP §2106.04. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Alternatively, the Office has long considered data gathering and data processing as well as data output recruitment information on a social network to be insignificant extra-solution activity, and these additional elements used to gather and output recruitment information on a social network are insignificant extra-solution limitations that do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(g). The current invention is directed to adjusting work order based on call consultation data, location data and maintenance evaluation value. When considered in combination, the claims do not amount to improvements of the functioning of a computer, or to any technology or technical field. Applicant's limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits.
Dependent claims 2-7, 9-14, and 16-20 merely incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claims 1, and 15 respectively, for example, claims 2, 7 and 20 sub-platforms, non-transitory and a model but these features only serve to further limit the abstract idea of independent claims 1, and 15 furthermore, merely using/applying in a computer environment such as merely using the computer as a tool to apply instructions of the abstract idea do nothing more than provide insignificant extra-solution activity since they amount to data gathering, analysis and outputting. Furthermore, they do not pertain to a technological problem being solved in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, and/or the limitations fail to achieve an actual improvement in computer functionality or improvement in specific technology other than using the computer as a tool to perform the abstract idea.
Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application.
Regarding Step 2B
Claims 1-7, 9-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element(s) as described above with respect to Step 2A Prong 2, the additional element of claims 1, and 15 include an Internet of Things (loT) system, user platform, service platform, management platform, sensor network platform, a terminal device, a communication network and gateway, device object sub-platform, sub-platform, the gas indoor device object sub-platform includes a metering device of a gas user, the gas pipeline network device object sub-platform includes at least one of a pressure regulation device, a gas gate station compressor, prediction model, and a machine learning model. The displaying interface and storing data merely amount to a general purpose computer used to apply the abstract idea(s) (MPEP 2106.05(f)) and/or performs insignificant extra-solution activity, e.g. data retrieval and storage, as described above (MPEP 2106.05(g)) which are further merely well-understood, routine, and conventional activit(ies) as evidenced by MPEP 2106.06(05)(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser’s back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to adjusting work order based on call consultation data, location data and maintenance evaluation value.
Claims 1-7, 9-20 is accordingly rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Allowable Subject Matter
Claims 1-7, 9-20 are allowable over the prior art, however, these claims remain rejected under 35 USC 101. Furthermore, if these claims overcome the 101 rejection, they would be allowable only if rewritten in independent form to include all the limitations of the base claim and any intervening claims.
Closest prior art to the invention include Tarabzouni et al. US 2010/0250312: System to facilitate pipeline management, program product, and related methods, Sahni et al. US 2020/0160252: Method and system for providing a multi-dimensional human resource allocation adviser and Wang J, Shuai Y, Feng C, Zhang P, Wang T, Lin N, Liu Z. Multi-dimensional mechanical response of multiple longitudinally aligned dents on pipelines and its effect on pipe integrity. Thin-Walled Structures. 2021 Sep 1;166:108020. None of the prior art of record, taken individually or in combination, teach, inter alia, teaches the claimed invention as detailed in the independent claims, input the user-side gas feature data, the gas composition features, the gas entry features, and the gas upstream transportation features into a gas fault prediction model to predict a gas fault type, the gas fault prediction model being a machine learning model; generate a location accuracy of one or more location feature vectors based on the gas fault type and an actual gas fault type, the one or more location feature vectors corresponding to the one or more maintenance feature vectors, for each of a plurality of dimensions, determine a weight for the one or more maintenance feature vectors in the dimension based on the location accuracy of the one or more location feature vectors corresponding to the one or more maintenance feature vectors in the dimension; and determine a maintenance assessment value for the dimension based on the first maintenance assessment value of the one or more maintenance feature vectors in the dimension and the weight for the one or more maintenance feature vectors;”. The reason to no applying the 35 USC 103 rejection of claims 1-7, 9-20 in the instant application is because the prior art of record fails to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Upon further searching the examiner could not identify any prior art to teach these limitations. The prior art on record, alone or in combination, neither anticipates, reasonably teaches, not renders obvious the Applicant’s claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang J, Shuai Y, Feng C, Zhang P, Wang T, Lin N, Liu Z. Multi-dimensional mechanical response of multiple longitudinally aligned dents on pipelines and its effect on pipe integrity. Thin-Walled Structures. 2021 Sep 1;166:108020.
Zhang S, Tong F, Li M, Jin S, Li Z. Research on multi-dimensional optimal location selection of maintenance station based on big data of vehicle trajectory. Entropy. 2021 Apr 21;23(5):495.
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THIS ACTION IS MADE FINAL. 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.
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/HAMZEH OBAID/Primary Examiner, Art Unit 3624
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