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 . Claims 1-10 have been canceled. Claims 11-38 are pending.
Drawings
Applicant’s replace figure filed 4/18/26 is acceptable and has been entered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 11: a receiver configured to receive, from an optical fiber laid on a wind turbine, an optical signal associated with the wind turbine;
Claim 11: a generator configured to generate sensor time-series data indicative of a state of the wind turbine;
Claim 11: a learning module configured to process the sensor time-series data and output processed sensor data including at least one sensor feature vector;
Claim 11: a large language model (LLM) module configured to process textual data comprising at least one of maintenance logs, operational notes, or alarm messages and output interpreted textual data including at least one text feature vector;
Claim 11: a predictive monitoring module configured to receive the sensor feature vector and the text feature vector, perform feature fusion between the sensor feature vector and the text feature vector, generate a prediction indicative of a potential maintenance issue based on the feature fusion, and trigger alert generation;
Claim 25: a receiver configured to receive, from an optical fiber laid on a wind turbine, an optical signal associated with the wind turbine;
Claim 25: a sensor data processor configured to generate sensor time-series data indicative of a state of the wind turbine based on the optical signal;
Claim 25: a deep learning network configured to process the sensor time-series data and output processed sensor data including at least one sensor feature vector;
Claim 25: a Large Language Model (LLM) configured to process textual data comprising at least one of maintenance logs, operational notes, or alarm messages and output interpreted textual data including at least one text feature vector;
Claim 25: a predictive monitoring processor configured to receive the sensor feature vector and the text feature vector, perform feature fusion between the sensor feature vector and the text feature vector, generate a prediction indicative of a potential maintenance issue based on the feature fusion, and trigger alert generation.
Support is found in [0029], [0049]-[0055] of applicant’s specification as filed. (Examiner has not been able to find support for “a predictive monitoring module [processor] configured to receive the sensor feature vector and the text feature vector, perform feature fusion between the sensor feature vector and the text feature vector, generate a prediction indicative of a potential maintenance issue based on the feature fusion, and trigger alert generation.” Specifically, the specification omits any steps and/or algorithms associated with performing sensor/feature fusion. See rejections below.)
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 11-38 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
With respect to claim limitations –
Claim 11: a predictive monitoring module configured to receive the sensor feature vector and the text feature vector, perform feature fusion between the sensor feature vector and the text feature vector, generate a prediction indicative of a potential maintenance issue based on the feature fusion, and trigger alert generation;
Claim 25: a predictive monitoring processor configured to receive the sensor feature vector and the text feature vector, perform feature fusion between the sensor feature vector and the text feature vector, generate a prediction indicative of a potential maintenance issue based on the feature fusion, and trigger alert generation;
Claims 24 and 38: fusing the sensor feature vector and the text feature vector to generate a prediction indicative of a potential maintenance issue –
the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Specifically, examiner has not been able to find support for “a predictive monitoring module [processor] configured to receive the sensor feature vector and the text feature vector, perform feature fusion between the sensor feature vector and the text feature vector, generate a prediction indicative of a potential maintenance issue based on the feature fusion, and trigger alert generation” (claims 1 and 25) and “fusing the sensor feature vector and the text feature vector to generate a prediction indicative of a potential maintenance issue” (claims 24 and 38). The specification omits any steps and/or algorithms associated with performing sensor/feature fusion.
Further, given the lack of written description, the claims are also considered as introducing new matter, given that they are not supported by the specification as originally filed.
The dependent claims are rejected by virtue of their dependency. Accordingly, 11-38 are rejected under 35 U.S.C. 112(a).
Claim Rejections - 35 USC § 112(b)
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.
Claims 11-23 and 25-37 are 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations –
Claim 11: a predictive monitoring module configured to receive the sensor feature vector and the text feature vector, perform feature fusion between the sensor feature vector and the text feature vector, generate a prediction indicative of a potential maintenance issue based on the feature fusion, and trigger alert generation;
Claim 25: a predictive monitoring processor configured to receive the sensor feature vector and the text feature vector, perform feature fusion between the sensor feature vector and the text feature vector, generate a prediction indicative of a potential maintenance issue based on the feature fusion, and trigger alert generation –
invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Specifically, examiner has not been able to find support for “a predictive monitoring module [processor] configured to receive the sensor feature vector and the text feature vector, perform feature fusion between the sensor feature vector and the text feature vector, generate a prediction indicative of a potential maintenance issue based on the feature fusion, and trigger alert generation,” as recited in claims 11 and 25. The specification omits any steps and/or algorithms associated with generating time-series data or performing sensor/feature fusion.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The dependent claims are rejected by virtue of their dependency. Accordingly, claims 11-23 and 25-37 are rejected under 35 U.S.C. 112(b).
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 11-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claims 11 and 25 are to a system (i.e., a machine), and claims 24 and 38 to a method (i.e., a process), thereby passing Step 1. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 11 and 25 is (text in brackets specific to claim 25):
generate sensor time-series data indicative of a state of the wind turbine [based on the optical signal];
process the sensor time-series data and output processed sensor data including at least one sensor feature vector;
process textual data comprising at least one of maintenance logs, operational notes, or alarm messages and output interpreted textual data including at least one text feature vector; and
receive the sensor feature vector and the text feature vector, perform feature fusion between the sensor feature vector and the text feature vector, generate a prediction indicative of a potential maintenance issue based on the feature fusion, and trigger alert generation.
The abstract idea of claims 24 and 38 is (text in brackets specific to claim 38):
generating sensor time-series data [based on the optical signal];
processing the sensor time-series data to generate a sensor feature vector;
processing textual data to generate a text feature vector;
associating and/or aligning the sensor time-series data and the textual data using at least one of a component identifier or timestamps;
fusing the sensor feature vector and the text feature vector to generate a prediction indicative of a potential maintenance issue; and
generating an alert based on the prediction.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level: generating time-series data and corresponding sensor data; processing textual data and synthesizing it with the sensor data to generate a prediction; and generating an alert based on the prediction. These are steps that an administrator could perform manually, given multiple data sets. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions pertaining to discerning maintenance issues, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. This is further supported by [0004] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 11 recites the following additional elements: a receiver configured to receive, from an optical fiber laid on a wind turbine, an optical signal associated with the wind turbine; a generator configured to; a learning module configured to; a large language model (LLM) module configured to; a predictive monitoring module configured to.
Claim 24 recites the following additional elements: receiving an optical signal from an optical fiber laid on a wind turbine; using a learning module; using a large language model.
Claim 25 recites the following additional elements: a receiver configured to receive, from an optical fiber laid on a wind turbine, an optical signal associated with the wind turbine; a sensor data processor configured to; a deep learning network configured to; a large language model (LLM) module configured to; a predictive monitoring processor configured to.
Claim 38 recites the following additional elements: receiving an optical signal from an optical fiber laid on a wind turbine; using a deep learning network; using a Large Language Model (LLM).
The elements above, with the exception of “a receiver configured to receive, from an optical fiber laid on a wind turbine, an optical signal associated with the wind turbine” and “receiving an optical signal from an optical fiber laid on a wind turbine,” are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0021], [0051], and [0077]-[0081] of applicant’s specification as filed, for example.
Examiner interprets “a receiver configured to receive, from an optical fiber laid on a wind turbine, an optical signal associated with the wind turbine” and “receiving an optical signal from an optical fiber laid on a wind turbine” as doing no more than generally linking the abstract idea to a field of use, i.e., wind turbines. See MPEP 2106.05(h).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea and tied to said field of use. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f), (h).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f), and/or generally linked to a field of use, as described in MPEP 2106.05(h).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea and tied to said field of use. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 12-13, 16-23, 26-27, and 30-37 further narrow the abstract idea above with additional abstract steps and/or information. This narrowing of the abstract idea does not integrate the abstract idea into practical and/or add significantly more.
Dependent claims 14-15 and 28-29, in addition to narrowing the abstract idea above with additional steps, recites further additional elements: wherein the LLM [module] comprises tokenization, embedding, transformer layers, and domain-specific fine-tuning; wherein the learning module [deep learning network] architecture comprises at least one convolution layer, at least one pooling layer, a flatten layer, an LSTM layer, and at least one fully connected layer. These provide nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f). Whether viewed alone or in combination, this does not integrate the abstract idea into practical application and/or add significantly more.
Accordingly, claims 11-38 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant’s remarks filed 4/18/26 have been fully considered. Examiner’s response follows.
Claim Rejections - 35 USC § 112
Applicant’s cancellation of claims 1-10 in their entirety has overcome the previous rejections under 35 USC § 112. Note, however, the new rejections under 35 USC § 112, which are made in view of the amendments.
Claim Rejections - 35 USC § 101
Applicant offers, regarding the rejections under 35 USC § 101:
Applicant respectfully traverses this rejection in view of newly presented Claims 11-38. Under the MPEP 2106.04(d) (the "Desjardins Check"), a claim is eligible if it reflects a specific technical improvement to a computer or technological process.
New independent Claims 11, 24, 25, and 38 are directed to a highly specific, technological architecture comprising a "predictive monitoring module" configured to perform "feature fusion" between a "sensor feature vector" (derived from high-dimensional time-series optical data) and a "text feature vector" (derived from unstructured text logs via an LLM).
This is not a process that can be performed mentally or with pen and paper. Humans cannot natively compute, extract, and mathematically fuse high-dimensional feature vectors utilizing convolutional and transformer-based architectures. The claimed invention addresses a specific technical problem in wind turbine monitoring: the inability to accurately predict faults when physical sensor data and textual maintenance logs are unsynchronized and in incompatible formats. The claimed solution-algorithmically fusing these distinct vectors to trigger automated predictive alerts- integrates the components into a practical, technological application that improves the functioning of the diagnostic system itself. Accordingly, the claims do not recite an abstract idea and are eligible under 35 U.S.C. § 101.
While well taken, examiner remains unpersuaded. Applicant has not arrived at a technical improvement, as asserted. Rather, applicant’s improvement, if one exists, pertains to maintenance management, i.e., an abstract idea. This is contrary to applicant’s assertion: “The claimed solution-algorithmically fusing these distinct vectors to trigger automated predictive alerts- integrates the components into a practical, technological application that improves the functioning of the diagnostic system itself.” MPEP 2106 is clear, however, that an improvement to abstract idea is still abstract.
The additional elements of the claim, e.g., a “predictive monitoring module,” are claimed and described in a results-oriented manner. MPEP 2106.05(a), with reference to MPEP 2106.05(f), is clear that this results-oriented claiming is equivalent to “apply it” and doesn’t integrate into practical application and/or add significantly more, either.
Accordingly, examiner maintains the rejections under 35 USC § 101.
Claim Rejections - 35 USC § 103
Examiner has not been able to find the combination of features as claimed. The previous rejections under 35 USC § 103 are withdrawn. As indicated previously, the prior art teaches monitoring wind turbines with the use of sensor, the sensed data being subsequently synthesized with additional data:
Damgaard (US 20200141392), previously cited, teaches:
[0120] FIG. 2 illustrates an algorithm according to an embodiment of the invention. In step 1 the sensor 9E is measuring a component value 3 of a wind turbine component 1 and as soon as the measurement is made it is communicated to a wind turbine controller 10. This is preferably what is understood by real-time measuring and communication.
[0121] In step 2 sensors 9A-n measures different operation environment values 4A-n of a plurality of values of one or more operation environments 5A-n. These operation environments 5 could e.g. be wind turbine components 1 or values related to meteorology/environment within the wind turbine 2 or outside the wind turbine 2 having direct or indirect impact on the value of the measured component value 3. The measurements made in step 2 are as in step 1 measured and communicated to the wind turbine controller 10 in real-time.
[0122] Alternatively, the environment data interface 21 obtains the component value and operation environment value from a data storage or data logger.
[0123] In step 3 the algorithm 7 of the wind turbine controller 10 receives all operation environment values 4A-n from the sensors 9A-n obtained from the operation environments 5A-n. The algorithm 7 then estimates at least one estimated component value 6 of the at least one wind turbine component 1. Hence the estimated component value 6 could be considered as a representation of the measured component value 3. This means that at least in some situations if the sensor 9E fails the wind turbine controller 10 may continue operation of the wind turbine based on the estimated component value 6.
Wang (US 20200201950), previously cited, teaches:
[0063] FIG. 5 is a block diagram of a component failure prediction system 104 in some embodiments. The component failure prediction system 104 may predict a component failure ahead of the actual failure. The component failure prediction system 104 may train and evaluate any number of models that predict component failure. In some embodiments, the component failure prediction system 104 trains a set of component failure prediction models for each component or set of components using historical sensor data received from sensors of any number of electrical assets (e.g., including renewable energy electrical assets such as wind turbines). In some embodiments, each set of models predicts failure of a different component of the same or different electrical assets.
[0064] The component failure prediction system 104 may train different failure prediction models of a set using the same metrics from historical sensor data but with different lead times and with different amounts of historical sensor data (e.g., different amounts of lookback times). The component failure production system 104 may evaluate the failure prediction models of the set based on sensitivity, precision, and/or specificity for the different lookback and lead times. As a result, the component failure prediction system 104 may select a failure prediction model of a set of failure prediction models for each component type (e.g., bearing), component (e.g., specific bearing(s) in one or more assets), component group type (e.g., generator including two or more components), component group (e.g., specific generator(s) including two or more components in one or more assets), asset type (e.g., wind turbines), or group of assets (e.g., specific set of wind turbines).
[0065] Metrics used to evaluate performance (e.g., based on values from sensor readings and/or from the sensors themselves) may be the same for different components even if the sensor data from sensors of the different components is different. By standardizing metrics for evaluation, the component failure prediction system 104 may “tune” or change aspects of the failure prediction model and model training to accomplish the goals of acceptable accuracy with acceptable lead time before the predicted failure. This enable improved accuracy for different components of an electrical assets with improved time of prediction (e.g., longer prediction times is preferable).
The prior art also teaches processing sensor data with LLMs.
Poupyrev (US 20250068885), cited previously, teaches:
[0065] The server system 106 is remote from the sensors 102 and client devices 104, and enables real-time data communication with the sensors 102 and client devices 104. The server system 106 implements data processing tasks that cannot be or are preferably not completed locally by the sensors 102 or client devices 104. Specifically, the server system 106 applies an LLM 150 to process the sensor data 120 collected from the sensors and generate the user-defined output 130 associated with the sensor data 120. The LLM 150 is trained and executed on the server system 106. For example, the largest GPT-3 model uses 175 billion parameters, 96 self-attention layers, 2048 tokens window size of a mask, and 96 heads of self-attention per multi-head self-attention layer. In some implementations, the server system 106 offers a comprehensive solution to convert the sensor data 120 to the user-defined output 130. The LLM 150 is hosted and retrained by the server system 106 to process the sensor data 120 that is optionally preprocessed, with or without alternative inputs (e.g., different types of user prompt or content data). Conversely, in some implementations, the LLM 150 is provided by a third-party model supplier. For example, the third-party LLM 150 is an open source neural network model requiring inputs as a predefined format. The server system 106 includes a sensor server 106A and an LLM server 106B, which is controlled by the third-party model supplier. The sensor server 106A is focused on sensor data 120 collection and preprocessing and has no or limited control over the third-party LLM 150. The sensor data 120 is optionally preprocessed to generate data having the predefined format (e.g., one or more descriptive tags) required for an input in the third-party LLM 150. The sensor data 120 or the preprocessed data having the predefined format are provided to the LLM server 106B to generate the output 130, which is returned to the sensor server 106A for further processing or presentation to the client devices 104.
However, new claim 11 requires a predictive monitoring module configured to “perform feature fusion between the sensor feature vector and the text feature vector.” Poupyrev teaches using an LLM to process sensor data to generate a “user-friendly output (e.g., text, video, audio, program, user interface).” Poupyrev teaches away from the claimed invention by using the LLM merely as a translation layer for human consumption. Poupyrev does not disclose or suggest using an LLM to generate a “text feature vector” from maintenance logs, nor does it teach mathematically fusing that text vector with a physical sensor time-series vector to predict maintenance issues.
In an updated search, examiner identified the following references, which, while generally relevant to the field of endeavor, stop short of the specificity required by the claim, whether viewed alone or in combination with the previously cited references.
Wang (US 20200210537), which teaches: An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.
Wang (US 20200210538), which teaches: An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.
Kondejkar (US 20220187818), which teaches: Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.
Accordingly, the previous rejections under 35 USC § 103 are withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wang (US 20200210537), which teaches: An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.
Wang (US 20200210538), which teaches: An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.
Kondejkar (US 20220187818), which teaches: Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629
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