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 .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 05/21/2025, Applicant, on 08/19/2025, amended claims amends claims 1, 5, 8, 10-11, and 15; adds new claims 21 and 22; claims 2, 6, 7, 16, and 18 are cancelled. Claims 1, 3-5, 8-15, 17, and 19-22 are pending in this application and have been rejected below.
Response to Arguments
Applicant's arguments filed 08/19/2025 have been fully considered, but they are not fully persuasive. The 35 USC § 103 rejection has been overcome. However, the updated 35 USC § 101 rejection of claims 1, 3-5, 8-15, 17, and 19-22 are applied in light of Applicant's amendments.
The Applicant argues “the above-mentioned technical features, as a whole, integrate the process into practical applications. Through the gas device, other devices (i.e., a monitoring device, a temperature sensor, and a pressure sensor), and the terminal device, the demand information of at least one gas work order can be obtained, which is conducive to determining the accurate fulfillment mode and fulfillment plan of the gas work order in the subsequent process, and thus can make reasonable allocation of personnel and time.” (Remarks 08/19/2025)
In response, the Examiner respectfully disagrees. The claimed subject matter, is directed to an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which falls into the “Certain methods of organizing human activity” within the enumerated groupings of abstract ideas set forth in the 2019 PEG. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of organizing human activity or the mental processes grouping. Thus, the claim recites a mental process for performing certain methods of organizing human activity.
The claimed subject matter is merely claims a method for receiving and analyzing information regarding work orders. Although it may be intended to be performed in a digital environment, the claimed subject matter (as currently claimed in the independent claim) speaks to the determining and analyzing data. Such steps are not tied to the technological realm, but rather utilizing technology to perform the abstract idea. Additionally, the claimed subject matter can also be categorized as a Mental Process as it recites concepts performed in the human mind (observation and evaluation). The steps of receiving data, determining information, and generating a work order can be performed by a human (mental process/pen and paper). The practice of calculating information and constructing orders with set parameters and timelines can be performed without computers, and thus are not tied to technology nor improving technology.
The solution mentioned in the amended limitation is not implemented/integrated into technology and thus not an improvement to the technical field. Further, there is no integration into a practical application as the claims can be interpreted as humans per se, as the claims fail to tie the steps to technology; insignificant extra solution activities (which are merely calculating and/or analyzing data).
The steps relied upon by the Applicant as recited does not improve upon another technology, the functioning of the computer itself, or allow the computer to perform a function not previously performable by a computer. The Applicant is using generic computing components (processors and sensors) to perform in a generic/expected way (obtaining and analyzing data).The abstract idea is not particular to a technological environment, but is merely being applied to a computer realm. The process of calculating and analyzing data specifically for work orders, and performing additional analysis can be done without a computer, and thus the claims are not “necessarily rooted", but rather they are utilizing computer technology to perform the abstract idea. The Examiner does not recognize any elements of the Applicant's claims and/or specification that would improve or allow the computer to perform a function(s) not previously performable by the computer, or improve the functioning of the computer itself. It is insufficient to indicate that the claims are novel and non-obvious, and thus contain “something more.” Just because the components may perform a specialized function does not mean that that the computer components are specialized. As such the application of the abstract idea of collecting and analyzing data regarding a service system, and performing correlation analysis is insufficient to demonstrate an improvement to the technology.
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, 3-5, 8-15, 17, and 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1, 3-5, 8-15, 17, and 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “2019 Revised Patent Subject Matter Eligibility Guidance” (published on 1/7/2019 in Fed. Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the “2019 PEG”).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the claims are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One of 2019 PEG, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which falls into the “Certain methods of organizing human activity” within the enumerated groupings of abstract ideas set forth in the 2019 PEG. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of organizing human activity or the mental processes grouping. Thus, the claim recites a mental process for performing certain methods of organizing human activity.
The limitations reciting the abstract idea(s), as set forth in exemplary claim 1, are: obtaining demand information of at least one gas work order of a gas platform, wherein the demand information includes at least one of a demand type, a work order creation time, detection data, a gas component aging degree, gas user feedback information, user information, a demand location, and a demand status]],the smart gas object platform is configured to obtain at least one of the detection data and the gas component aging degree…determining, based on the demand information, a fulfillment mode of the at least one gas work order, wherein the fulfillment mode at least includes self-service fulfillment and manual fulfillment, and the manual fulfillment includes at least one of immediate manual fulfillment and manual fulfillment after supplementing information; in response to that the fulfillment mode is the self-service fulfillment, automatically fulfilling the fulfillment mode through a guidance of a client and the terminal device of the qas platform to enable the terminal device by the qas platform to independently operate and complete the at least one qas work order: and in response to that the fulfillment mode is the manual fulfillment, determining [[a]]the work order fulfillment plan of the at least one gas work order based on the demand information and personnel information of the gas platform, wherein the work order fulfillment plan includes a fulfillment time limit and fulfillment personnel, and the determining the work order fulfillment plan of the at least one gas work order based on the demand information and personnel information of the gas platform includes: determining a gas pipeline network complexity based on a pipeline branch point count, a qas user type, a qas user count, and a pipeline density of a qas pipeline network of an area where the demand location is located; and determining the fulfillment time limit based on the demand type, the detection data, the gas component aging degree, the user information, and the gas pipeline network complexity, wherein the fulfillment time limit includes at least one of a latest start time and a latest completion time of the at least one gas work order, and the user information includes at least one of the gas user type, a gas customer count, and a qas customer feature; wherein the determining the fulfillment time limit based on the demand type, the detection data, the gas component aging degree, the user information, and the qas pipeline network complexity includes: determining the fulfillment time limit through a fulfillment time limit prediction model based on the demand type, the detection data, the qas component aging degree, the user information, and the qas pipeline network complexity, wherein the fulfillment time limit prediction model is a machine learning model, the fulfillment time limit prediction model is obtained by training a plurality of first training samples with labels, and the training includes: inputting the plurality of first training samples with labels to an initial fulfillment time limit prediction model; contrasting a loss function through the labels and an output of the initial fulfillment time limit prediction model, iteratively updating a parameter of the initial fulfillment time limit prediction model based on the loss function, completing the training and obtaining a trained fulfillment time limit prediction model when the loss function of the initial fulfillment time limit prediction model satisfies a set condition; wherein the plurality of first training samples include the demand type, the detection data, the gas component aging degree, the user information, and the gas pipeline network complexity of a sample gas work order, the labels of the plurality of first training samples include a sample fulfillment time limit of the sample gas work order, the sample fulfillment time limit is less than a certain threshold, the plurality of first training sample are obtained based on historical data, the labels of the plurality of first training samples are obtained through manual labeling: uploading the fulfillment time limit and the fulfillment personnel to the terminal device of the smart gas user platform through the smart gas management platform: and instructinq the fulfillment personnel to perform the at least one gas work order based on the fulfillment time limit..
Independent claims 8 and 14 recite the CRM and system for performing the method of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two of the 2019 PEG, the judicial exception is not integrated into a practical application. The additional elements are directed to and transmit the at least one of the detection data and the gas component aging degree to the smart gas management platform through the smart qas sensor network platform, and the smart qas user platform is configured to obtain at least one of the demand type, the work order creation time, the qas user feedback information, the user information, the demand location, and the demand status, and transmit the at least one of the demand type, the work order creation time, the qas user feedback information, the user information, the demand location, and the demand status to the smart gas management platform through the smart gas sensor network platform… (as recited in the claims). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: and transmit the at least one of the detection data and the gas component aging degree to the smart gas management platform through the smart qas sensor network platform, and the smart qas user platform is configured to obtain at least one of the demand type, the work order creation time, the qas user feedback information, the user information, the demand location, and the demand status, and transmit the at least one of the demand type, the work order creation time, the qas user feedback information, the user information, the demand location, and the demand status to the smart gas management platform through the smart gas sensor network platform… (as recited in the claims) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
In addition, Applicant’s Specification (paragraph [0030]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
In addition, Applicant’s Specification (paragraph [0189]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (3-5, 8-14, 17, and 19-22) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 3-5, 8-14, 17, and 19-22 “a smart gas user platform, a smart gas service platform, a smart gas sensor network platform, and a smart gas object platform; the smart gas object platform is configured to obtain at least one of the detection data and the gas component aging degree, and transmit the at least one of the detection data and the gas component aging degree to the smart gas management platform through the smart gas sensor network platform; and the smart gas user platform is configured to obtain at least one of the demand type, the work order creation time, the gas user feedback information, the user information, the demand location, and the demand status, and transmit the at least one of the demand type, the work order creation time, the gas user feedback information, the user information, the demand location, and the demand status to the smart gas management platform through the smart gas sensor network platform; determining, based on the demand information, an emergency degree of the at least one gas work order; and determining, based on the emergency degree, the fulfillment mode; in response to that the fulfillment mode is the manual fulfillment, determining that the at least one gas work order adopts the immediate manual fulfillment or the manual fulfillment after supplementing information based on an information adequacy; wherein the information adequacy is relevant to a correction coefficient, and the correction coefficient is determined based on a gas pipeline network complexity; determining a gas pipeline network complexity based on a pipeline branch point count, a gas user type, a gas user count, and a pipeline density of a gas pipeline network of an area where the demand location is located; and determining the fulfillment time limit based on the demand type, the detection data, the gas component aging degree, the user information, and the gas pipeline network complexity, wherein the fulfillment time limit includes at least one of a latest start time and a latest completion time of the at least one gas work order, and the user information includes at least one of the gas user type, a gas customer count, and a gas customer feature; determining the fulfillment time limit through a fulfillment time limit prediction model based on the demand type, the detection data, the gas component aging degree, the user information, and the gas pipeline network complexity, wherein the fulfillment time limit prediction model is a machine learning model; wherein an input of the fulfillment time limit prediction model includes an emergency degree of the at least one gas work order; determining, based on the demand information and the emergency degree, a value loss of the at least one gas work order through the value loss layer, wherein the value loss layer is a machine learning model; and determining, based on the value loss, the demand type, the detection data, the gas component aging degree, the user information, and the gas pipeline network complexity, the fulfillment time limit through the fulfillment time limit prediction layer, wherein the fulfillment time limit prediction layer is a machine learning model; determining the fulfillment personnel based on the demand information and the personnel information of the gas platform, the fulfillment personnel including at least one of a fulfillment individual and a fulfillment team who fulfill the at least one gas work order, the personnel information including at least one of a proficiency degree of processing personnel and pending work order information of the processing personnel, and the proficiency degree of the processing personnel being relevant to a personnel rank, a historical gas work order count corresponding to the processing personnel, and a historical gas work order type distribution; determining, based on the demand information and the personnel information of the gas platform, the fulfillment personnel through a preset mode; wherein in the preset mode, a determination of the fulfillment personnel is relevant to a fulfillment value of the processing personnel, and the fulfillment value is relevant to the proficiency degree of the processing personnel, an emergency degree and a value loss of the at least one gas work order; wherein in the preset mode, a determination of the fulfillment personnel is relevant to a fulfillment man-hour cost of the processing personnel, and the fulfillment man-hour cost is relevant to the proficiency degree of the processing personnel and a gas demand fulfillment difficulty; and wherein, the gas demand fulfillment difficulty is obtained in a way including: constructing, based on the demand information, a demand information vector; determining, based on the demand information vector, at least one demand information reference vector through a vector database, wherein a similarity between the at least one demand information reference vector and the demand information vector satisfies a preset condition; and determining the gas demand fulfillment difficulty based on a fault point count and a maintenance complexity degree of a fault point of the at least one demand information reference vector; wherein the maintenance complexity degree of the fault point is determined based on at least one of a maintenance material quantity used in maintenance, a maintenance material type, and an information adequacy; wherein the gas platform includes the smart gas user platform, the smart gas service platform, the smart gas management platform, the smart gas sensor network platform, and the smart gas object platform; inputting the demand information and the emergency degree of a second training sample gas work order to the value loss layer to obtain a value loss of the second training sample gas work order output by the value loss layer; inputting the demand information and the emergency degree of a second training sample gas work order to the value loss layer to obtain a value loss of the second training sample gas work order output by the value loss layer; Inputting the value loss of the second training sample gas work order output by the value loss layer, the demand type, the detection data, the gas component aging degree, the user information, and the gas pipeline network complexity corresponding to the second training sample gas work order into the fulfillment time limit prediction layer to obtain the sample fulfillment time limit output by the fulfillment time limit prediction layer; constructing a second loss function based on the labels of the second training samples and an output result of the fulfillment time limit prediction layer; updating parameters of the value loss layer and the fulfillment time limit prediction layer until a second set condition is met and completing the joint training, wherein the labels of the second training samples are obtained based on the sample fulfillment time limit corresponding to the demand type, the detection data, the gas component aging degree, the user information, and the gas pipeline network complexity of the sample gas work order in the historical data.”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (16-19) recite the system for performing the method of claims 2-14 and 20. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are 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. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kumar; Awadhesh. METHOD AND SYSTEM FOR FACILITATING OPTIMIZATION OF ENERGY IN A DISTRIBUTED ENVIRONMENT, .U.S. PGPub 20180041032The present disclosure in general relates to a field of managing demand and supply of energy. More specifically, a method and a system for facilitating optimization of energy in a distributed environment.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/
Primary Examiner, Art Unit 3625
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