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 Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 (U.S. Pub. No. 2023/0281439 A1) in view of Digital Twin-based Anomaly Detection in Cyber-physical Systems By Qinghua Xu et al.
As to claim 1, Maher discloses 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 (0016);
apply a sensor data analyzer engine to the resource sensor data (0054);
determine, by the sensor data analyzer engine, whether at least one sensor anomaly of the resource sensor data is present (0076);
apply an on-demand synthetic data generator to the at least one sensor anomaly (0076);
generate, by the on-demand synthetic data generator, synthetic sensor data associated with the resource, wherein the synthetic sensor data is based on a pattern of the resource sensor data from the plurality of sensors (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 (0093).
While Maher teaches generating synthetic sensor based on resource sensor.
Maher is not specific to mention the use of real-time pattern.
Qinghua Xu et al. teaches wherein the synthetic sensor data is based on a real- time pattern of the resource sensor (pg. 205, col. 2).
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.
Independent claims 12 and 17 are directed to similar subject matter and are rejected under the same grounds of rejection.
As to claim 2, Maher as modified 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 0057); and
generate, based on the pattern of resource sensor data, the synthetic sensor data (Maher 0058).
Claims 13 and 18 are directed to similar subject matter and are rejected under the same grounds.
As to claim 3, Maher as modified 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 0056);
create a first training dataset comprising the resource sensor data associated with the at least one resource (Maher 0056); and
train the sensor data analyzer engine in a first stage using the first training dataset (Maher 0057).
As to claim 4, Maher as modified 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 0034).
As to claim 5, Maher as modified 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 (Qinghua Xu et al. 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 (Qinghua Xu et al. pg. 210, 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 (Qinghua Xu et al. pg. 211, col. 2).
Claims 14 and 19 are directed to similar subject matter and are rejected under the same grounds.
As to claim 6, Maher as modified 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 0093).
Claims 15 and 20 are directed to similar subject matter and are rejected under the same grounds.
As to claim 7, Maher as modified 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 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 (Qinghua Xu et al. pg. 210, cl. 2).
Claim 16 is directed to similar subject matter and is rejected under the same grounds.
As to claim 8, Maher as modified 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 0068, 0093).
As to claim 9, Maher as modified teaches The system of claim 1, wherein the plurality of sensors is configured to collect telemetry data (Maher 0052).
As to claim 10, Maher as modified 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 (Qinghua Xu et al. pg. 210, col. 2).
As to claim 11, Maher as modified 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 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 0093).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892 for list of completed relevant cited prior art.
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/NEVEEN ABEL JALIL/Supervisory Patent Examiner, Art Unit 2152 January 8, 2026