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 .
Claim Objection
2. Claim 1 is objected to because of the following informalities:
Claims 1 and 12, please change the word “occurrs” into -- occurs --.
Claim 12, there is insufficient antecedent basis for “the smart gas network device object platform”. Please change it into -- the smart gas pipeline network device object platform --.
Claim 12, there is insufficient antecedent basis for “the smart gas network device sensing network platform”. Please change it into -- the smart gas pipeline network device sensing network platform --.
Appropriate correction is required.
Rejections - 35 USC § 112
3. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.--The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
4. Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
In claims 1 and 12, the recitation of the phrases "training a plurality of second training samples with second labels" renders the claims indefinite. It is unclear whether said “training” refers to train “a plurality of second training samples with second labels” or the “failure risk prediction model”.
In claims 8 and 18, the recitation of the phrases "training a plurality of first training samples with first labels" renders the claims indefinite. It is unclear whether said “training” refers to train “a plurality of first training samples with first labels” or the “the gas supply pressure variation prediction model”.
Claims 2-7, 9-11, 13-17 and 19-20 are rejected by virtue of their dependency to claim 1 or 12.
Claim Rejections - 35 USC § 101
5. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 101 that form the basis for the rejections under this section made in this Office action:
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.
6. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the 2019 PEG (now been incorporated into MPEP 2106), the revised procedure for determining whether a claim is "directed to" a judicial exception requires a two-prong inquiry into whether the claim recites: (1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human interactions such as a fundamental economic practice, or mental processes); and (2) additional elements that integrate the judicial exception into a practical application (see MPEP § 2106.05(a)-(c), (e)-(h)).
Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look to whether the claim: (3) adds a specific limitation beyond the judicial exception that is not "well-understood, routine, conventional" in the field (see MPEP § 2106.0S(d)); or (4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Claims 1-20 are directed to an abstract algorithm of assessing a pipeline failure based on a machine learning model.
Specifically, representative claim 12 recites:
A system for assessing a pipeline failure based on a smart gas Internet of Things (IoT), comprising:
a smart gas user platform, wherein the smart gas user platform includes a plurality of smart gas user sub-platforms;
a smart gas service platform, wherein the smart gas service platform includes a plurality of smart gas service sub-platforms;
a smart gas safety management platform, wherein the smart gas safety management platform includes a plurality of smart gas pipeline network safety management sub-platforms and a smart gas data center;
a smart gas pipeline network device object platform, wherein the smart gas (pipeline) network device object platform is configured to obtain gas monitoring data based on a data obtaining instruction; and
a smart gas pipeline network device sensing network platform, wherein the smart gas (pipeline) network device sensing network platform is configured to interact with the smart gas data center and the smart gas (pipeline) network device object platform;
wherein the smart gas safety management platform is configured to:
obtain at least one first failure risk in a gas pipeline and a downstream user feature from the smart gas data center, wherein the downstream user feature is obtained based on an interaction between the smart gas safety management platform and a smart gas data center, and the at least one first failure risk is determined based on gas pipeline data, gas transmission data, and historical failure data of the gas pipeline;
generate a plurality of candidate gas processing schemes based on the at least one first failure risk, wherein at least one of the candidate gas processing schemes at least includes a gas repair sub-scheme, and the gas repair sub-scheme includes a gas disconnection repair sub-scheme and a pressure reduction reinforcement repair sub-scheme;
determine a gas supply pressure variation distribution corresponding to the at least one of the candidate gas processing schemes based on the at least one first failure risk and the at least one of the candidate gas processing schemes;
construct a gas supply pressure feature graph, wherein the gas supply pressure feature graph includes a first gas supply pressure feature graph and a second gas supply pressure feature graph, the second gas supply pressure feature graph is constructed at least based on the gas supply pressure variation distribution, the second gas supply pressure feature graph includes a second node and a second edge, the second node includes a connection position of pipelines, the second edge includes a pipeline, the second edge is a directed edge, a direction of the second edge is configured to reflect a direction of gas flow, an edge feature of the second edge includes a gas supply pressure, a pipeline service life, the historical failure data, a gas supply pressure variation, a failure risk, and a downstream gas supply feature, and the gas supply pressure variation is determined based on the gas supply pressure variation distribution; and
determine at least one second failure risk through a failure risk prediction model based on the second gas supply pressure feature graph, wherein the failure risk prediction model is a machine learning model,
wherein the failure risk prediction model is obtained by training a plurality of second training samples with second labels through the smart gas safety management platform, a second training sample includes a historical second gas supply pressure feature graph, a second label includes whether a node or an edge corresponding to the historical second gas supply pressure feature graph has a failure and a pipeline parameter of a gas pipeline where the failure occurs, and wherein the second failure risk is configured to assess a potential failure risk of the gas pipeline after being processed based on the at least one of the candidate gas processing scheme.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
The highlighted portion of the claim constitutes an abstract idea under the 2019 Revised Patent Subject Matter Eligibility Guidance and the additional elements are NOT sufficient to amount to significantly more than the judicial exceptions, as analyzed below:
Step
Analysis
1. Statutory Category ?
Yes.
System
2A - Prong 1: Judicial Exception Recited?
Yes.
See bolded portion listed above.
Under the broadest reasonable interpretation, each of the limitations (a), (b), (c) and (d) encompasses processes of data acquisition, analysis, and/or manipulation based on predetermined rules/options which can be performed in the human mind or by a human with the aid of pan and paper using mental steps. According to MPEP 2106.04(a)(2).III: The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. See CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). Also, according to the 2019 PEG: “If a claim, under its broadest reasonable interpretation, covers performance in the mind but for the recitation of generic computer components, then it is still in the mental processes category unless the claim cannot practically be performed in the mind. See Intellectual Ventures | LLC v. Symantec Corp., 838 F.3d 1307, 1318 (Fed. Cir. 2016).
Under the BRI, the limitation (e) encompasses a process of data manipulation, evaluation and/or judgment that can be performed by a human using mental steps with the aid of pan and paper. The “failure risk prediction model” or “machine learning model” is recited at a high level of generality. The claim does not provide any detail of how the “machine learning model” operates to generate/predict the “at least one second failure risk”. Rather, the combination of limitation (e) only recites the outcome of a computing scheme which is used like a “black box AI” whose internal workings are a mystery of math concepts to its users. In light of the USPTO’s July 17, 2024 Subject Matter Eligibility Examples (e.g., Examples 47-49), a computing scheme (e.g., prediction) using a machine learning model is considered an "abstract idea" if the claim focuses solely on the concept of performing the computing using a generic machine learning algorithm without any specific technical improvements or applications that go beyond the basic idea of using a computer to analyze data and generate predictions. As such, the recitation of the “machine learning model” does not negate the mental nature of the limitation (e) because the claim here merely uses the “machine learning model” as a tool to perform the otherwise mental processes. See also MPEP 2106.05(f).
Under its BRI, the limitation (f) encompasses processes of binning/clustering/labelling existing training data and training the “failure risk prediction model” or “machine learning model” using the labelled training data. In light of the USPTO’s July 2024 Subject Matter Eligibility Examples (e.g., Example 47, claim 2), discretizing training data to generate input data by processes including binning or clustering continuous data may be practically performed in the human mind using observation, evaluation, judgment, and opinion, while said training is recited at a high level of generality which may involve optimizing the machine learning model using a series of mathematical calculations to iteratively adjust the algorithms and/or parameter values of the machine learning model, therefore encompasses mathematical concepts.
As to the data characterization of the recited “first failure risk in a gas pipeline”, “a downstream user feature”, “wherein at least one of the candidate gas processing schemes at least includes …”, “a gas supply pressure variation distribution corresponding to the at least one of the candidate gas processing schemes”, “a gas supply pressure feature graph”, etc., they are considered merely descriptive of the information being observed/determined, thus to be further part of the mental processes and/or field use limitations of the mental processes recited in instant claim 12. See MPEP 2106.05(h).
Nothing in the bolded portion precludes the limitations (a), (b), (c), (d), (e) and (f) from practically being performed in the mind or by a human with the aid of pen/paper or a general-purpose computer. Therefore, the combination of the bolded limitations constitutes an abstract idea that falls within a combination of the “Mental Process” and the “Mathematical Concepts” Groupings of Abstract Ideas defined by the 2019 PEG.
2A - Prong 2: Integrated into a Practical Application?
No.
The claim as a whole does not integrate the abstract idea into a practical application.
The preamble of the claim recites generally “a smart gas Internet of Things (IoT)”, which is not qualified for a meaningful limitation because it only links the use of the judicial exception to a particular technological environment.
The claimed system comprises additional elements including: “a smart gas user platform”, “a plurality of smart gas user sub-platforms”, “a smart gas service platform”, “a plurality of smart gas service sub-platforms”, “a smart gas safety management platform”, “a plurality of smart gas pipeline network safety management sub-platforms”, “a smart gas data center”, “a smart gas pipeline network device object platform”, and “a smart gas pipeline network device sensing network platform”. In the computing ecosystem, a platform refers commonly to the foundational environment that supports the execution of applications or software. As such, under the BRI, the combination of the various “platforms” and “sub-platforms” encompasses basic hardware, operating system, or frameworks of a general-purpose computer upon which other applications, processes, or technologies can be developed or run. According to the MPEP 2106.04(a)(2), if a claim limitation, under its broadest reasonable interpretation, covers mental processes except for the mention of generic computer components performing computing activities via basic function of the computer, then the claim is likely considered to be directed to an ineligible abstract idea, as it essentially describes a mental process that could be performed by a human without the computer components adding any significant practical application beyond the abstract concept itself. Accordingly, none of recited platforms is considered to be qualified for a meaningful limitation to integrate the identified abstract idea into a practical application.
Under its BRI, the “smart gas data center” could read on a basic data storage of a general-purpose computer. The acts of “obtain gas monitoring data …” and “obtain at least one first failure risk in a gas pipeline and a downstream user feature from the smart gas data center …”, etc. encompass merely processes of gathering the data and information necessary for performing the abstract idea but not requiring the use of any particular type of sensor or device at a particular location of the pipeline (or even the use of any sensor at all), as the data of “at least one first failure risk in a gas pipeline and a downstream user feature” could just be “received” by a computer from, e.g., look-up tables as opposed to the generation of actual measurement data in real-time. According to MPEP 2106.05(g)(3): … that were described as mere data gathering in conjunction with a law of nature or abstract idea. See also Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 13863, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). As such, it represents an extra-solution activity to the judicial exception.
In general, the claim as a whole does not meet any of the following criteria to integrate the abstract idea into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. However, in all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the algorithm across a wide range of applications.
2B: Claim provides an Inventive Concept?
No.
At Step 2B, evaluations are given to the additional limitations/elements to determine whether they amount to an inventive concept by considering them both individually and in combination.
In the instant case, focusing on what the inventors have invented exactly, Examiner considers that the “core” of the pending claim 12 is directed to an algorithm of assessing a pipeline failure using a machine learning model which falls within a combination of the mental process and the math concept groupings of abstract ideas under the 2019 PEG. None of the claimed additional limitations/elements makes the claim significantly more than the abstract idea. Furthermore, under the BRI, it is deemed that all the claimed additional limitations/elements recite activities that are all well-understood, routine, conventional in the art (see the prior art references cited in sections 7 below), they do not provide any inventive concept or reflect a qualified improvement. The combination of these additional limitations/elements reads on nothing more than a generic computing system with a “black-box AI” machine learning model.
In particular, neither the claims nor the Specification provides sufficient disclosure regarding any improvement to how a machine learning algorithm can be trained to obtain the failure risk prediction model or the gas supply pressure variation prediction model, but simply discloses a high-level generic recitation that a machine learning algorithm is being trained (see Spec. para. [0089] and [0102]). There is insufficient evidence from the Specification to indicate that said training and/or the use of the trained machine learning model involves anything other than the generic application of a known technique in its normal, routine, and ordinary capacity or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. As such, the Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. See also MPEP § 2106.05(f).
The claim is therefore ineligible under 35 USC 101.
The dependent claims 13-20 inherit attributes of the independent claim 12, and do not add anything which would render the claimed invention a patent eligible application of the abstract idea. These claims merely extend (or narrow) the abstract idea which do not amount for "significantly more" or reflect an “inventive concept” because they merely add details to the algorithm which forms the abstract idea as discussed above.
In particular, claim 18 recites: … the smart gas safety management platform is configured to: determine the gas supply pressure variation distribution through a gas supply pressure variation prediction model based on the first gas supply pressure feature graph, wherein the first gas supply pressure feature graph includes a first node and a first edge, the first node includes a connection position of pipelines, and the first edge includes a pipeline, and the gas supply pressure variation prediction model is a machine learning model, wherein the gas supply pressure variation prediction model is obtained by training a plurality of first training samples with first labels through the smart gas safety management platform, a first training sample includes a historical first gas supply pressure feature graph, and a first label includes a gas supply pressure variation of a node or an edge corresponding to the historical first gas supply pressure feature graph.
Under its BRI, “determine the gas supply pressure variation distribution through a gas supply pressure variation prediction model based on the first gas supply pressure feature graph …” encompasses a processes of data analysis and/or manipulation which can be performed in the human mind or by a human using mental steps with the aid of pan and paper. The generally recited gas supply pressure variation prediction model or machine learning model is simply used as a tool to perform the otherwise mental processes. The training of the machine learning model and/or the use of the machine learning model do not involve anything other than the generic application of a known technique in its normal, routine, and ordinary capacity or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. Accordingly, claim 18 is directed to a judicial exception (an abstract idea) without significantly more.
Claims 1-12 are rejected for the same reason as for claims 12-20 above.
Hence the claims 1-20 are treated as ineligible subject matter under 35 U.S.C. § 101.
Examiner’s Note
7. Claims 1-20 would be allowable if rewritten or amended to overcome the objection and rejection as set forth in sections 2-6 above in this Office action.
While there are related references that discuss method/system for assessing a pipeline failure risks based on smart gas Internet of Things (loT), the prior art of record do not specifically provide teachings for those limitations including: construct a gas supply pressure feature graph, wherein the gas supply pressure feature graph includes a first gas supply pressure feature graph and a second gas supply pressure feature graph, the second gas supply pressure feature graph is constructed at least based on the gas supply pressure variation distribution, the second gas supply pressure feature graph includes a second node and a second edge, the second node includes a connection position of pipelines, the second edge includes a pipeline, the second edge is a directed edge, a direction of the second edge is configured to reflect a direction of gas flow, an edge feature of the second edge includes a gas supply pressure, a pipeline service life, the historical failure data, a gas supply pressure variation, a failure risk, and a downstream gas supply feature, and the gas supply pressure variation is determined based on the gas supply pressure variation distribution; and determine at least one second failure risk through a failure risk prediction model based on the second gas supply pressure feature graph, wherein the failure risk prediction model is a machine learning model, the failure risk prediction model is obtained by training a plurality of second training samples with second labels through the smart gas safety management platform, a second training sample includes a historical second gas supply pressure feature graph, a second label includes whether a node or an edge corresponding to the historical second gas supply pressure feature graph has a failure and a pipeline parameter of a gas pipeline where the failure occurs, and wherein the second failure risk is configured to assess a potential failure risk of the gas pipeline after being processed based on the at least one of the candidate gas processing scheme. It is these limitations found in each of the claims 1-20 in combination with the rest of the limitations as recited in independent claim 1 or 12 that have not been found, taught or suggested by the prior art of record, which make these claims distinguish over the prior art.
Citation of Relevant Prior Art
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shao (US 20200309632 A1) discloses a gas leakage detection method based on a compound IoT and an IoT system, wherein a gas pipeline of a user has gas leakage or the failure risk is judged/assessed via a user participation manner, the judgment manner is intelligent (Fig. 2).
Li et al. (CN 115577744 A, machine translation) discloses an intelligent prediction method of gas pipeline leakage fire risk comprising: collecting oil gas pipeline leakage fire risk prediction basic data; designing the limited ridge wave neural network; based on the optimization of the limited ridge wave parameter neural network the improved firefly algorithm; oil gas pipeline leakage fire risk level prediction. The intelligent prediction method of oil gas pipeline leakage fire risk using the above structure can efficiently and accurately predict the risk level of the oil gas pipeline leakage fire, provide beneficial support for the emergency management of the oil gas pipeline.
Madeira et al. (US 20230204166 A1) discloses a technique for monitoring oil and gas pipelines in real time with immediate warning of leak events, followed by a procedure for geographically locating the leak position, as well as its start time, comprising: providing the use of usual operational sensors of pressure, temperature, flow and specific mass already installed and available in oil and gas pipelines and methods of statistical inference, and optimization and artificial intelligence that allow detection and location of leaks in pipelines. The invention has the following components: sensor communication module (1); statistical tools for compensation of measurement and model uncertainties (2); automatic leak detection techniques (3); leak locator (4); graphical user interface (5); measuring sensors (6); flow simulator (7); historical database (8).
Gao et al. (CN 115965822 A, machine translation) discloses a classification method of risk level and model training method, relating to the technical field of pipeline non-destructive detection.
Hong et al. (CN 113780237 A, machine translation) discloses an early warning method for preventing external force damage of underground pipeline, device and system thereof. The invention can timely and effectively judge whether there is dangerous device capable of damaging the external force to the underground pipeline, entering the environment of the underground pipeline, so as to working staff take measures to prevent possible damage action, so as to protect the underground pipeline.
Pawaskar et al. -- Smart Piped Natural Gas PNG Meter with loT Technology;
Wanasinghe et al. -- The Internet of Things in the Oil and Gas Industry A Systematic Review;
Ekka et al. -- An Effective Early Detection and Prediction System for Gas Leakage in Smart Environments;
Contact Information
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIUQIN SUN whose telephone number is (571)272-2280. The examiner can normally be reached 9:30am-6:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached at (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/X.S/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
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