Prosecution Insights
Last updated: April 19, 2026
Application No. 18/286,578

SYSTEM AND METHOD OF MONITORING AN INDUSTRIAL ENVIRONMENT

Non-Final OA §101
Filed
Oct 12, 2023
Examiner
DESTA, ELIAS
Art Unit
2857
Tech Center
2800 — Semiconductors & Electrical Systems
Assignee
Siemens Aktiengesellschaft
OA Round
1 (Non-Final)
84%
Grant Probability
Favorable
1-2
OA Rounds
2y 11m
To Grant
94%
With Interview

Examiner Intelligence

Grants 84% — above average
84%
Career Allow Rate
886 granted / 1055 resolved
+16.0% vs TC avg
Moderate +10% lift
Without
With
+9.5%
Interview Lift
resolved cases with interview
Typical timeline
2y 11m
Avg Prosecution
33 currently pending
Career history
1088
Total Applications
across all art units

Statute-Specific Performance

§101
25.9%
-14.1% vs TC avg
§103
26.8%
-13.2% vs TC avg
§102
19.0%
-21.0% vs TC avg
§112
18.6%
-21.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 1055 resolved cases

Office Action

§101
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 . Preliminary Amendment The preliminary amendment filed on October 12, 2023 is accepted by the Examiner. Claims 1-20 are pending in the application. IDS The information disclosure statements (IDS) submitted on October 12, 2023 and November 27, 2023 are being considered by the Examiner. Drawing The drawing filed on October 12, 2023 is objected to because of the following minor issue: it would be better illustration if figures 4 and 5 be labeled as to their function. Specification The preliminary amendment to the specification submitted on October 12, 2023 is accepted by the Examiner; however, the specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. Claim Objection Claim 17 is objected to because of the following minor informality: in page 27, last line into the claim “perform at least one of the method” should be “perform at least one of the [steps].” Claim rejection – 35 U.S.C. §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 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to judicial exception (i.e., abstract idea) without significantly more. The requirement for subject matter eligibility test for products and processes requires first, the claimed invention must be to one of the four statutory categories. 35 U.S.C. §101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter. The latter three categories define "things" or "products" while the first category defines "actions" (i.e., inventions that consist of a series of steps or acts to be performed). Second, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception. The judicial exceptions (also called "judicially recognized exceptions" or simply "exceptions") are subject matter that the courts have found to be outside of, or exceptions to, the four statutory categories of invention, and are limited to abstract ideas, laws of nature and natural phenomena (including products of nature). In the first step, it is to be determined whether the patent claim under examination is directed to an abstract idea. If so, in the second step of analysis, it is to be determined whether the patent adds to the idea “something more” or "significantly more” that embodies an “inventive concept.” In the instant case, claim 1 is representative and it is reproduced here with the limitations that are part of the abstract idea in bold: A method of monitoring an industrial environment, the method comprising: classifying datapoints of an industrial dataset associated with the industrial environment into one or more classes using a trained model wherein the trained model is generated using training dataset, wherein the classes are associated with at least one of a physical quality identifier, one of a location identifier and a device identifier associated with generation of the datapoints, and a unit identifier of the datapoints; augmenting the industrial dataset, based on the classifying to include at least one of the physical quality identifier, the location identifier, the device identifier, and the unit identifier along with an associated confidence metric represented as a percentage indicating confidence of the classifying; and monitoring operating conditions of at least one asset or at least one process in the industrial environment using the augmented industrial dataset. Step 2A: Prong I: The claim recites the steps of “classifying datapoints of an industrial dataset associated with the industrial environment into one or more classes”, “the classes are associated with at least one of a physical quality identifier, one of a location identifier and a device identifier associated with generation of the datapoints, and a unit identifier of the datapoints”, “augmenting the industrial dataset, based on the classifying to include at least one of the physical quality identifier, the location identifier, the device identifier, and the unit identifier along with an associated confidence metric represented as a percentage indicating confidence of the classifying’. These limitations could be carried out as a purely mental process (at least in a some relatively simple situations) and/or they could amount to a mathematical calculation (representing values in percentage requires simple arithmetic). Therefore, the recited method falls in the abstract idea grouping of mental processes and/or mathematical concepts at Prong 1 of the §101 analysis. Prong II: This abstract idea is not integrated into a practical application at Prong 2 of the §101 analysis because the claim does not recite sufficient additional elements to integrate the abstract idea into a practical application. The claim recites the method comprising the additional element steps of " using a trained model wherein the trained model is generated using training dataset’ and “monitoring operating conditions of at least one asset or at least one process”. However, training a generic model to follow through a trend on a high level of generality, and monitoring parameter of values that would be generic in nature, such as augmented industrial data sets is not considered to be extra-solution activity. The courts have found that adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea (such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)) is not enough to integrate the abstract idea into a particular practical application or make the claim qualify as “significantly more” (see MPEP § 2106.05(g)). The claim does not recite applying the abstract idea with, or by use of, any particular machine, nor does the claim affect a real-world transformation or reduction of a particular article to a different state or thing. The claim amounts to manipulating data: “classifying datapoints of an industrial dataset associated with industrial environment into one or more classes”, “augmenting the industrial datasets” and “monitoring operating conditions of at least one asset …using the augmented datasets”. The claim does not recite any particular real-world actions that are taken as a result of the “monitoring”. The claim provides “a monitoring of industrial environment” as the general field-of-use, but does not recite a particular practical application being carried out within that field-of-use. Therefore, the claimed invention does not appear to be limited to the use of the mental process or math in a particular practical application, but instead the claim appears to monopolize the mental process or math itself, in any practical application where it might conceivably be used. Step 2B: Finally, at Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea for the same reasons as discussed above with regard to Prong 2. Claim 1 is rejected as ineligible under 35 USC §101. Claims 18 and 19: are analogous to claim 1, except the respective claims additionally recite a system having a memory, a processor and a machine-readable instruction and a computer program product comprising a computer-readable hardware storage device with a processor and computer code respectively. These additional elements are separate from the abstract idea that need to be considered at Prong 2 of the §101 analysis. However, these additional elements are merely generic computer processing components that are invoked as a tool to perform the abstract idea, which does not cause the claim as a whole to integrate the abstract idea into a particular practical application or provide significantly more than the recited abstract idea. Claims 18 and 19 are therefore rejected as ineligible under 35 USC §101 as well. Claim 20: the instant claim is directed to the derivation of the datapoints which merely adds to the insignificant extra-solution activity being recited which is actually a mental process as the act of generic data gathering at a high level of generality. Dependent claim 2: the instant claim is directed to the industrial datasets and would be considered data gathering at a highest level of generality. Dependent claim 3: the instant claim is directed to the industrial datasets comprising historical sensor datapoints from industrial environment and the metadata to the classes and described as data gathering at a highest level of generality. Dependent claim 4: the instant claim is directed to mapping of datapoints which is a human thought process at least in a relatively small sample situation. Dependent claim 5: the instant claim is directed to filtering, extrapolating and resampling of data points would be carried out with mathematical algorithm, and would be considered data analysis at a highest level of generality. Dependent claim 6: the instant claim is directed to extracting features from the data points and would be carried out with mathematical algorithm, and would be considered data analysis at a highest level of generality. Dependent claim 7: the instant claim is directed to processing of vector values and would be carried out with mathematical algorithm, and would be considered data analysis at a highest level of generality. Dependent claim 8: the instant claim is directed to classifying data points and would be carried out with mathematical algorithm, and would be considered data analysis at a highest level of generality. Dependent claim 9: the instant claim is directed to determining confidence metric and would be carried out with mathematical algorithm, and would be considered data analysis at a highest level of generality. Dependent claim 10: the instant claim is directed to classifying datapoints and would be carried out with mathematical algorithm, and would be considered data analysis at a highest level of generality. Dependent claim 11: the instant claim is directed to displaying classes, validating classes and retraining models and would be carried out with mathematical algorithm, and would be considered data analysis at a highest level of generality. Dependent clam 12: the instant claim is directed to training the model that would be used for the analysis and would be carried out with mathematical algorithm, and would be considered data analysis at a highest level of generality. Dependent claim 13: the instant claim is directed to auto-encoding or neural network for the purposes of carrying out unsupervised learning; however, these additional elements are merely generic algorithms that are invoked as a tool to perform the abstract idea, which does not cause the claim as a whole to integrate the abstract idea into a particular practical application or provide significantly more than the recited abstract idea. Dependent claim 14: the instant claim is directed to processing vector as related to datapoints, and would be carried out with mathematical algorithm, and would be considered data analysis at a highest level of generality. Dependent claim 15: the instant claim is directed to displaying an outcome of a comparison; however, it is a tool to perform the abstract idea, which does not cause the claim as a whole to integrate the abstract idea into a particular practical application or provide significantly more than the recited abstract idea. Dependent claim 16: the instant claim is directed to modifying parameter values and would be considered data analysis at a highest level of generality. Dependent claim 17: the instant claim is directed to receiving industrial datasets and carrying out the steps in claim 1 which is insignificant extra-solution activity. Art of Interest In reference to claims 1-20: Paulitsch et al. (U.S. PAP 2023/0267368, hereon Paulitsch) discloses system of detecting at least one abnormal datapoint in operation data associated with an industrial environment. The system comprising iteratively applying one or more anomaly detection models to at least one subset of the operation data, wherein the anomaly detection models are trained based on a training dataset consisting of datapoints labeled as normal; classifying subset-datapoints in the subset as one of normal datapoints, and abnormal datapoints using the anomaly detection models; updating the training dataset at least with the normal datapoints; retraining the anomaly detection models with the updated training dataset after expiration of a threshold time (see Paulitsch, Abstract). The instant claim differs in that “the classes are associated with at least one of a physical quality identifier, one of a location identifier and a device identifier associated with generation of the datapoints, and a unit identifier of the datapoints” and “augmenting the industrial datasets based on” those values “along with an associated confidence metric represented as a percentage indicting confidence of classifying” in combination with the rest of the claim limitations as claimed and defined by the Applicants. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Cella et al. (U.S. PAP 2020/0225655, hereon Cella) discloses a method for receiving, by the processing system, reporting packets from one or more respective sensors of the plurality of sensors. Each reporting packet is sent from a respective sensor and indicates sensor data captured by the respective sensor; performing, by the processing system, one or more edge operations on one or more instances of sensor data received in the reporting packets (see Cella, Abstract). Trinh et al. (U.S. Patent No. 11,307,570, hereon Trinh) discloses a computer-implemented predictive maintenance process that includes receiving a set of sensor data generated from sensors associated with equipment, one of the sensors being a target sensor, the set of sensor data comprising measured values of the target sensor. The process may also include selecting a subset of sensor data, the subset of sensor data comprising data generated from the sensors and excluding the measured values of the target sensor (see Trinh, Fig. 5 and Abstract). Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIAS DESTA whose telephone number is (571)272-2214. The examiner can normally be reached M-F: 8:30 to 5:00 pm. 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, Andrew M Schechter can be reached at 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ELIAS DESTA/ Primary Examiner, Art Unit 2857
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Prosecution Timeline

Oct 12, 2023
Application Filed
Jan 08, 2026
Non-Final Rejection — §101 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
84%
Grant Probability
94%
With Interview (+9.5%)
2y 11m
Median Time to Grant
Low
PTA Risk
Based on 1055 resolved cases by this examiner. Grant probability derived from career allow rate.

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