DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Response to Arguments
3. Applicant's arguments received 03/20/2026 have been considered but are moot in view of the new ground(s) of rejection. Detailed response is given in sections 4-5 as set forth below in this Office Action.
Applicant argues that:
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Applicant's arguments in reference to the Ex Parte Desjardins (Appeal No. 2024-000567) have been fully considered, but they are not persuasive. Examiner reminds to the Applicant that during patent examination, the pending claims must be given the broadest reasonable interpretation consistent with the specification. Under a broadest reasonable interpretation (BRI), words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. The plain meaning of a term means the ordinary and customary meaning given to the term by those of ordinary skill in the art at the relevant time. See MPEP 2111.01. Moreover, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
With these principles in mind, Examiner maintains the position that: focusing on what the inventors have invented exactly, the pending claims 1-2, 6, 8-9, 18-19 and 21-40 of the present application are directed to an abstract idea of determining an alarm of anomalous conditions in a pipeline/flowline without significantly more.
Particular evaluation has been given to the newly added limitation “(d) training the artificial neural network using the probability metric and signals received from the one or more sensors” in light of the decision provided by Ex Parte Desjardins. However, the decision in that case is fact specific and is not analogous to the pending claims of the present application.
Specifically, Ex Parte Desjardins clarified that claims for training machine learning models can be patent-eligible if they integrate an abstract idea into a practical application and demonstrate technological improvements. The USPTO Appeals Review Panel (ARP) found that the claims integrated the abstract idea into a practical application by reciting the additional limitation “training the machine learning model on the second machine learning task by training the machine learning model on the second training data to adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task.” The ARP affirmed that at least the above limitation of independent claim 1 reflects improvements in how the machine learning model operates, such as reduced storage, system simplification.
In the instant case, the claimed strategy of training the “the artificial neural network” requires merely “using the probability metric and signals received from the one or more sensors.” The training algorithm itself is recited at a high level of generality, while the Spec. (e.g., para. [0034]) indicates that the training is implemented using a backpropagation algorithm through which the neural network's weights are optimized. When given its BRI in light of the background, the backpropagation algorithm is mathematical calculations. Therefore, the limitation “(d) training …” encompasses mathematical concepts. See also USPTO’s July 2024 Subject Matter Eligibility Examples (e.g., Examples 47, claim 2). Put it differently, Ex parte Desjardins relates to improvements in machine learning technology. The pending claims of the present application recite merely an abstract idea (math + mental) but without including any additional limitation/element that would integrate the abstract idea into a practical application or reflect a qualified improvement. Neither the claims nor the Spec. specifies and/or demonstrates that the claimed training of the artificial neural network reflects a new and useful improvement which involved memory savings and reduced system complexity, as identified by the Ex Parte Desjardins decision. Accordingly, the pending claims are not analogous to the claims at issue in Ex parte Desjardin.
The rest of the Applicant’s arguments are reliant upon the issues discussed above or have been fully addressed by the analysis under the 2019 PEG as set forth below in the previous Office Action.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 1-2, 6, 8-9, 18-19, and 21-40 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 1-2, 6, 8-9, 18-19, and 21-40 describe an abstract idea of determining an alarm of anomalous conditions in a pipeline or a flowline. Specifically, representative claim 1 recites:
A method for detecting an anomaly in a pipeline or a flowline, the method comprising:
measuring one or more properties in the pipeline or the flowline via one or more sensors; monitoring real-time data in the pipeline or the flowline based on signals received from the one or more sensors, wherein the pipeline or flowline includes a plurality of nodes, the nodes including at least one or more inlets and one or more outlets;
generating a probability metric based on the real-time data using a prediction service, wherein the prediction service uses a multi-branch artificial neural network having at least one convolution layer, wherein a first branch of the multi-branch artificial neural network includes a plurality of first features and wherein a second branch of the multi-branch artificial neural network includes a plurality of second features, wherein the first branch receives input parameters of at least a relative flow rate difference and a change in number of inlets of the pipeline or flowline, wherein the relative flow rate difference is the relative flow rate difference of a fluid in the pipeline or flowline, wherein the fluid in the pipeline or flowline comprises at least one hydrocarbon, and wherein the probability metric indicates whether an anomaly is present;
(c) storing the probability metric in a memory;
(d) training the artificial neural network using the probability metric and signals received from the one or more sensors;
(e) determining whether to add an alarm, based, at least in part on the probability metric, wherein the alarm responds to one or more received signals from the pipeline or flowline; and if there are one or more active alarms, performing an action based on the active alarm.
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:
1. Statutory Category ?
Yes.
Method
2A - Prong 1:
Judicial Exception Recited?
Yes.
See bolded limitations above.
Under the broadest reasonable interpretation (BRI), the "generating" limitation (b) merely employes mathematical relationships to manipulate existing information to generate additional information. In particular, this limitation recites generating a probability metric by means of "a multi-branch artificial neural network having at least one convolution layer." Referring to the Specification (e.g., para. [0036-[0038] and [0041]), the multi-branch artificial neural network having at least one convolution layer encompasses the solution to specific mathematical formulas to generate the "probability metric" value. The limitation (b) thus falls within the “Mathematical Concepts” Grouping of Abstract Ideas.
Under the BRI, the "storing" limitation (e) encompasses a mental process that can be performed by a human using pen and paper or a general-purpose computer.
Under the BRI, the "training" limitation (d) encompasses a mental process of generic data processing that can be performed by a human using a general-purpose computer. The training is implemented using a backpropagation algorithm through which the neural network's weights are optimized (see Spec. [0034]). When given its BRI in light of the background, the backpropagation algorithm is mathematical calculations. Therefore, it encompasses mathematical concepts. See USPTO’s July 2024 Subject Matter Eligibility Examples (e.g., Examples 47, claim 2).
Under the BRI, the "determining" limitation (e) encompasses mental processes, i.e. data manipulation, evaluation and judgment, that can be performed in the human mind or by a human using a pen and paper. Note, claims can recite a mental process even if they are claimed as being performed on a computer. The courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind. See MPEP 2106.04(a)(2).
The "determining" limitation (d) further recites, "if there are one or more active alarms, performing an action based on the active alarm." This latter portion of the "determining" limitation is entirely conditional, depending on whether there are active alarms, but is no longer linked to the remainder of the "determining" limitation or the generated probability metric. Thus, the claim encompasses determining not to add an alarm, and the "performing" limitation may not occur; and if it does occur, it is no longer linked to the "generating" or "determining" limitations of claim 1. See Spec. para. [0047]. In particular, a computer programmer's mental decision whether to add an alarm can be performed in the human mind, or by a human based on basic critical thinking (or with the aid of pen and paper), thus falls within the mental process groupings of abstract ideas because it covers concepts performed in the human mind.
Therefore, the bolded portion of instant claim 1, reciting a series of mathematical concepts and mental process, amounts to an abstract idea falling within a combination of the “Mental Process” and “Mathematical Concepts” groupings of Abstract Ideas defined by the 2019 PEG.
2A - Prong 2:
Integrated into a Practical Application?
No.
Under the BRI, the limitation (a) reads on merely a process step of gathering the data/information necessary for performing the abstract idea. It does not require
any particular devices or sensors that are specifically configured to perform the "measuring", while "monitoring real-time data" can be done with the aid of pen and paper. Further, the "pipeline or flowline" is not specified and
neither is "the nodes including at least one or more inlets and one or more outlets" (pipeline nodes are commonly known as the physical machines that host one or more pipeline processes; pipeline inlet can have many different functions or structures depending on its purpose such as filling, allowing stormwater to flow into a facility, pumping stations, bringing high-pressure gas/flow from a place to another, etc.). As such, claim 1 would monopolize the identified abstract idea across a wide range of applications rather than integrate the judicial exception into a particular
practical application.
As to the data characteristics recited in the limitation (b),
Under the BRI, they merely inherit attributes of the claimed abstract idea but are not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. Specifically, the claimed data characteristics encompass both pipelines and flowlines and a wide variety of fluids, including fluids that contain at least one hydrocarbon and those that are mostly water and may or may not contain any hydrocarbons (see Spec. [0015]). However, the claim does not link the composition of the fluid to any other claim limitation. Further, claim 1 recites observing "a relative flow rate difference and a change in number of inlets of the pipeline or flowline," but in the context of the multi-branch calculations performed by the artificial neural network. These limitations evidence that instant claim 1 recites mathematical concepts, rather than merely involves them.
The claim as a whole does not meet any of the following criteria:
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. At most, it only generally links the judicial exception to a particular technological environment or field of use. See MPEP 2106.04(d)(2).
2B: Claim provides an Inventive Concept?
No.
Focusing on what the inventors have invented exactly, it is deemed that the “core” of the representative claim 1 is directed to an algorithm of determining an alarm of anomalous conditions in a pipeline or a flowline based on a trained AI model. Under the BRI, the claimed algorithm falls within a combination of the “Mental Process” and “Mathematical Concepts” groupings of abstract ideas. The claim does not recite any additional limitation that would reflect an inventive concept or amount to more than mere instructions to apply the judicial exception using generic computer components.
In particular, the limitations of measuring one or more properties in the pipeline or the flowline via one or more sensors, and monitoring real-time data in the pipeline or the flowline based on signals received from the one or more sensors, as well as the data characteristics of the pipeline or flowline including a plurality of nodes, the nodes including at least one or more inlets and one or more outlets, etc. are all deemed to be “well-understood, routine, and/or conventional activity in the field. They do not add any inventive concept which would amount for "significantly more". Furthermore, the conditional statement of performing an action based on the active alarm is generically recited w/o details so it is an insignificant post solution activity and/or a field of use limitation, which does not reflect a qualified improvement. See MPEP 2106.05.
The claim is therefore ineligible under 35 USC 101.
The dependent claims 2, 6, and 8-9 inherit attributes of the independent claim 1, but do not add anything which would render the claimed invention a patent eligible application of the abstract idea. The claims merely extend (or narrow) the abstract idea which do not amount for "significant more" because they merely add details to the algorithm which forms the abstract idea as discussed above.
Claims 18-19 and 21-40 are rejected for the same reason as for claims 1-2, 6, and 8-9. In particular, claims 18-19 and 21-40 recite abstract ideas of predicting an alarm of anomalous conditions in a pipeline or a flowline wherein a majority of the fluid is water, then triggering an alarm. The claims are not integrated into a particular practical application. The recitation of the generic computer adapted for performing the abstract algorithm is "well-understood, routine, conventional" in the field. Moreover, similar to claims 1-2, 6 and 8-9, there is no additional elements in the claims that are significantly more than the abstract idea.
Hence claims 1-2, 6, 8-9, 18-19, and 21-40 are treated as ineligible subject matter under 35 USC 101.
Examiner’s Note
6. While there are related references that discuss techniques of detecting an anomaly in a pipeline or a flowline based on artificial intelligence models, the prior art of record do not specifically provide teachings for method/system of generating a probability metric using a prediction service, wherein the prediction service uses a multi-branch convolutional neural network (CNN) having at least one convolution layer, wherein a first branch of the multi-branch CNN includes a plurality of first features and wherein a second branch of the multi-branch CNN includes a plurality of second features, wherein the first branch receives input parameters of at least a relative flow rate difference and a change in number of inlets of the pipeline or flowline, wherein the relative flow rate difference is the relative flow rate difference of a fluid in the pipeline or flowline, and wherein the fluid comprises at least one hydrocarbon or a majority of the fluid is water. It is these limitations found in each of the claims 1-2, 6, 8-9, 18-19 and 21-40, as they are recited in independent claim 1, 18, 31 or 36, respectively, that would make these claims distinguish over the prior art.
Contact Information
7. 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 on (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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