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 .
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 Donnelly (US 9,989,645) in view of Islam (US 2024/0130621).
Regarding claims 1, 15, 16, 20, Donnelly discloses an anomaly detection method (col. 20, lines 52-67),
comprising: receiving image data from one or more cameras configured to capture an image of a
physical asset (col. 13, lines 53-65); and outputting an alert when the probability of the physical anomaly
being present in the image data exceeds a threshold value (col. 15, lines 36-67; col. 21, lines 1-7).
Donnelly discloses all the limitations set forth above but fails to explicitly disclose determining a
probability of a physical anomaly being present in the image data using an artificial intelligence model
that is trained to detect anomalous image data, wherein the artificial intelligence model is trained using
image data representing normal operating conditions of the physical asset.
However, Islam discloses determining a probability of a physical anomaly being present in the image
data using an artificial intelligence model that is trained to detect anomalous image data, wherein the
artificial intelligence model is trained using image data representing normal operating conditions of the
physical asset (under different lighting conditions of the structures of objects in images in page 70, [0557-0559]; page 74, [0579]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was
first filed to incorporate the features of Islam within the system of Donnelly in order to accurately
identify anomalous object thereby improving the reliability of the system.
Regarding claim 2, Donnelly discloses analyzing the image data to determine that the physical anomaly
is a specific type of anomaly; and outputting the alert including information on the specific type of
anomaly present in the image data (col. 15, lines 53-67).
Regarding claim 3, Donnelly discloses wherein analyzing the image data to determine that the physical
anomaly is the specific type of anomaly comprises determining a probability that the physical anomaly is
the specific type of anomaly using one or more secondary artificial intelligence models that are trained
to predict the physical anomaly as being one or more types of anomalies (col. 15, lines 36-67).
Regarding claims 4-5, 17, Donnelly and Islam disclose all the limitations set forth in claim 1 and Islam
further discloses wherein the one or more secondary artificial intelligence models comprise a model that
is trained to perform image segmentation on the image data to classify the anomaly as being the specific
type of anomaly (page 74, [0579]).
Regarding claims 6, 18-19, Donnelly and Islam disclose all the limitations set forth in claim 1 and
Islam further discloses receiving a context of the image data, wherein the multi-modal generative Al
model uses the context of the image data to determine the probability that the physical anomaly is the
specific type of anomaly (page 70, [0557-0559]; page 74, [0579]).
Regarding claim 7, Donnelly and Islam disclose all the limitations set forth in claim 1 and Islam further
discloses receiving audio data and/or vibration data associated with the physical asset, wherein the
multi-modal generative Al model uses the received audio and/or vibration data to determine the
probability that the physical anomaly is the specific type of anomaly (page 70, [0557-0559]; page 74, [0579]).
Regarding claim 8, Donnelly and Islam disclose all the limitations set forth in claim 1 and Islam further
discloses wherein the multi-modal generative Al model is configured to generate an output comprising
one or both of: a description of the specific type of anomaly, and a suggested action for responding to
the specific type of anomaly (page 70, [0557-0559]; page 74, [0579]).
Regarding claim 9, Donnelly discloses wherein determining that the physical anomaly is the specific type
of anomaly comprises applying one or more rules to the image data (col. 15, lines 36-67).
Regarding claim 10, Donnelly discloses receiving auxiliary data associated with the physical asset from
one or more sensors, and wherein determining that the physical anomaly is the specific type of anomaly
is further based on the auxiliary data (col. 15, lines 21-67).
Regarding claim 11, Donnelly discloses wherein analyzing the image data to determine that the physical
anomaly is the specific type of anomaly is performed automatically when the probability that the
physical anomaly is present in the image data exceeds the threshold value (col. 15, lines 21-67).
Regarding claim 12, Donnelly discloses wherein analyzing the image data to determine that the physical
anomaly is a specific type of anomaly is performed in response to a prompt to identify the specific type
of anomaly (col. 15, lines 21-67).
Regarding claims 13-14, Donnelly and Islam disclose all the limitations set forth in claim 1 and Islam
further discloses receiving user feedback on the specific type of anomaly, and updating the one or more
secondary artificial intelligence models based on the user feedback (page 70, [0557-0559], page 74, [0579]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Khare et al. (US 2024/0212845) discloses animal data-based method.
Benkert et al. (US 2024/0249118) discloses data mining and applications.
Farid et al. (US 2024/0017743) discloses task-relevant machines.
Reynolds (US 5,889,550) discloses camera tracking system.
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DP
March 12, 2026
/DANIEL PREVIL/ Primary Examiner, Art Unit 2685
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