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 .
Status of Claims
This action is in response to the amendments filed 04/13/2026. Claims 1, 12, and 17 have been amended, claims 1-20 are currently pending.
Response to Arguments
Applicant’s arguments regarding the prior art rejection have been fully considered but are moot because of the new ground(s) of rejection. Applicant argues that the cited prior art references do not teach “generate, by the on-demand synthetic data generator, synthetic sensor data to replace the at least one sensor anomaly”. Examiner notes that the Darvishi reference has been brought in to teach a method of replacing a faulty (i.e., anomalous) sensor, with the previously cited Maher and Xu references. The prior art rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Maher (US 20230281439 A1, herein Maher) in view of Xu et al (“Digital Twin-based Anomaly Detection in Cyber-physical Systems”, herein Xu), in further view of Darvishi et al, (“A Machine-Learning Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins”, herein Darvishi).
Regarding claim 1, Maher teaches a system for generating a digital twin of a resource using partial sensor data and artificial intelligence, the system comprising:
a memory device with computer-readable program code stored thereon;
at least one processing device operatively coupled to the at least one memory device and the at least one communication device, wherein executing the computer-readable code is configured to cause the at least one processing device to:
receive resource sensor data from a plurality of sensors, wherein the plurality of sensors is associated with a resource (para. [0016]);
apply a sensor data analyzer engine to the resource sensor data (para. [0054]);
determine, by the sensor data analyzer engine, whether at least one sensor anomaly of the resource sensor data is present; apply an on-demand synthetic data generator to the at least one sensor anomaly (para. [0076]);
generate, by the on-demand synthetic data generator, synthetic sensor data [to replace the at least one sensor anomaly], wherein the synthetic sensor data is based on a [real-time] pattern of the resource sensor data from the plurality of sensors (para. [0024]); and
generate, based on the resource sensor data from the plurality of sensors and the synthetic sensor data, a digital twin of the resource (para. [0093]).
While Maher teaches generating synthetic sensor based on resource sensor, Maher does not explicitly teach wherein the synthetic sensor data is based on a real-time pattern of the resource sensor.
Xu teaches wherein the synthetic sensor data is based on a real-time pattern of the resource sensor (pg. 205, col. 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify Maher’s system of synthetic data for monitored sensor equipment with real-life patterns generated in real-time because it provides for accuracy and currency.
While the combination of Maher and Xu teaches generating synthetic sensor data based on resource sensor data, the combination of Maher and Xu does not explicitly teach generating synthetic sensor data to replace the at least one sensor anomaly.
Darvishi teaches generating synthetic sensor data to replace the at least one sensor anomaly (at least section I A para. 1 and para. 8, section II D para. 2, and section II E para. 4-5 all teach replacing input data that was determined to be faulty, or anomalous, with estimated, or generated synthetic data. For example, section I A para. 8 explicitly states “The proposed structure consists of a set of estimators (each associated with a sensor) providing residual signals as well as replacements (estimates) for faulty data”)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify Maher’s system of synthetic data for monitored sensor equipment (as modified by Xu) with the faulty sensor replacement method from Darvishi to provide the data synthesis and classification components from Maher with residual signals that are easier to interpret, as stated in at least section V C paragraph 3 of Darvishi.
As to claim 2, the combination of Maher, Xu, and Darvishi teaches the system of claim 1, wherein the on-demand synthetic data generator is configured to:
generate a pattern of resource sensor data based on the resource sensor data (Maher para. [0057]); and
generate, based on the pattern of resource sensor data, the synthetic sensor data (Maher para. [0058]).
As to claim 3, the combination of Maher, Xu, and Darvishi teaches the system of claim 1, wherein the processing device is further configured to:
identify resource sensor data associated with at least one resource (Maher para. [0056]);
create a first training dataset comprising the resource sensor data associated with the at least one resource (Maher para. [0056]); and
train the sensor data analyzer engine in a first stage using the first training dataset (Maher para. [0057]).
As to claim 4, the combination of Maher, Xu, and Darvishi teaches the system of claim 3, wherein the resource sensor data associated with the at least one resource comprises at least one of resource sensor data for one resource or resource sensor data for a plurality of resources (Maher para. [0034]).
As to claim 5, the combination of Maher, Xu, and Darvishi teaches the system of claim 1, wherein the processing device is further configured to:
apply, in response to the generation of the digital twin, at least one of an augmented data or an event simulation to the digital twin (Xu pg. 206, col. 1);
test the digital twin based on the application of the at least one of the augmented data or the event simulation to generate at least one digital twin metric (Xu pg. 211, col. 2); and
compare the at least one digital twin metric to an acceptable metric threshold to determine whether the at least one digital twin metric meets the acceptable metric threshold (Xu pg. 211, col. 2).
As to claim 6, the combination of Maher, Xu, and Darvishi teaches the system of claim 5, wherein the processing device is further configured to implement, in response to the at least one digital twin metric meeting the acceptable metric threshold, the digital twin to a digital environment (Maher para. [0093]).
As to claim 7, the combination of Maher, Xu, and Darvishi teaches the system of claim 5, wherein the processing device is further configured to:
regenerate, in response to the at least one digital twin metric not meeting the acceptable metric threshold, an updated synthetic sensor data by the on-demand synthetic data generator (Maher para. [0056]); and
generate, based on the resource sensor data from the plurality of sensors and the updated synthetic sensor data, an updated digital twin of the resource (Xu pg. 210, col. 2).
As to claim 8, the combination of Maher, Xu, and Darvishi teaches the system of claim 5, wherein the acceptable metric threshold is based on at least one of a similar digital twin associated with a similar resource of the resource (Maher para. [0068], para. [0093]).
As to claim 9, the combination of Maher, Xu, and Darvishi teaches the system of claim 1, wherein the plurality of sensors is configured to collect telemetry data (Maher para. [0052]).
As to claim 10, the combination of Maher, Xu, and Darvishi teaches the system of claim 1, wherein the processing device is further configured to determine the presence of at least one sensor anomaly based on comparing each resource sensor data of each sensor to each resource sensor data of the plurality of sensors associated with the resource (Xu pg. 210, col. 2).
As to claim 11, the combination of Maher, Xu, and Darvishi teaches the system of claim 1, wherein the processing device is further configured to:
determine, by the sensor data analyzer engine, the resource sensor data from the plurality of sensors do not comprise the at least one sensor anomaly (Maher para. [0126], it is unclear how the determination is made, so concept of anomaly detection reads on “determination” and reaching successful data set); and
generate, based on the resource sensor data from the plurality of sensors, the digital twin of the resource (Maher para. [0093]).
Independent claims 12 and 17 are directed to similar subject matter as recited in claim 1 and are rejected under the same grounds of rejection.
Claims 13 and 18 are directed to similar subject matter as recited in claim 2 and are rejected under the same grounds.
Claims 14 and 19 are directed to similar subject matter as recited in claim 5 and are rejected under the same grounds.
Claims 15 and 20 are directed to similar subject matter as recited in claim 6 and are rejected under the same grounds.
Claim 16 is directed to similar subject matter as recited in claim 7 and is rejected under the same grounds.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190294975 A1 (Sachs) teaches a method for aggregating one or more digital twins in a distributed inference process to classify events and determine potential relationships between different digital twins.
US 20190362268 A1 (Fogarty et al) teaches a method for synthesizing data such that a subset of outlier data is replaced by matched counterparts in the top deciles with determined propensity scores.
US 20230152757 A1 (Lee et al) teaches a method for executing a digital twin including a virtual representation of a piece of equipment and generating an indication of a fault or a diagnosis of the fault for the one or more pieces of equipment.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/L.M.F./ Examiner, Art Unit 2147
/MARC S SOMERS/ Primary Examiner, Art Unit 2159
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