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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/12/26 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-6, 8-10, 12, 13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Francis et al (US Pub. 2022/0277217) in view of Qi et al (US Pub. 2025/0078516) and Brisebois et al (US Pub. 2017/0329972).
With respect to claim 1, Francis discloses A method comprising: capturing, via one or more cameras, plurality of images over time from a site, (see figure 3A, 302);
transmitting the plurality of images to a hub, (see figure 1, numerical 3, and paragraph 0015, wherein …supply all image data collectively to the evaluation device 3…);
querying, in real-time, at least one sensor and/or related sensor data for at least one attribute of at least one element, the at least one element identified in at least one of the plurality of images, (see ‘217 paragraph 0016, wherein …moving object can be detected “one attribute of at least one element”...);
tracking,
ascertaining, in real-time, a state of an environment by integrating the at least one attribute and the spatio-temporal attributes of the at least one element, the environment associated with the site,
applying, in real-time, an integrated reasoning model to the state of the environment to identify one or more events that may occur and/or are occurring at the site that require operator intervention, the system identifies a specific type of scene, it may generate environmental control commands. Such commands could include providing alerts or begin recording data based on the type of scene identified. In another embodiment, an alarm may be output, recording may begin, etc.), as claimed.
However, Francis fails to disclose tracking, using a convolution neural network,...in real-time, spatio-temporal attributes of the at least one element; wherein integrating uses a dynamic Bayesian network; and wherein the integrated reasoning model incorporates a Bayesian probabilistic model or a data-driven model, as claimed.
Qi teaches tracking, using a convolution neural network,...in real-time, spatio-temporal attributes of the at least one element, (see paragraph 0008, wherein ...A spatio-temporal information matrix is constructed by using a track detection result... A detection network is constructed by using mixture of Gaussians (MOG2) background modeling and a faster region-convolutional neural network (R-CNN)...), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of object detection and tracking for surveillance using image analysis. Teaching of Qi can be incorporated in to the Francis system as suggested (see paragraph 12, ...neural networks..), for suggestion, and modifying the system yields a intelligent video analysis (see Qi paragraph 0002), for motivation.
And, Brisebois teaches integrating uses a dynamic Bayesian network; and wherein the integrated reasoning model incorporates a Bayesian probabilistic model or a data-driven model, (see paragraph 0019, Machine learning based on Bayesian reasoning may be used to derive an inference of risk. In Bayesian reasoning, to evaluate the probability of a hypothesis, a prior probability is initially assigned to the hypothesis. The prior probability may later be updated to a posterior probability in light of new, relevant data. For example, a machine learning algorithm, such as a classifier, may be used to analyze an event log (e.g., based on Bayesian probabilities) to determine a risk associated with the event that caused the event log. The risk assessment engine may determine a risk assessment and send the risk assessment to the DLP system to perform triage (e.g., classification and assessment) of the DLP event with the revised threat assessment), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of event detection using image analysis. Teaching of Brisebois to use Bayesian models can be incorporated in to the Francis system as suggested (see figure 3A, numerical 317 reasoning engine), for suggestion, and modifying the system yields an event analysis (see Brisebois paragraph 0019), for motivation.
With respect to claim 2, combination of Francis, Qi and Brisebois further discloses wherein the plurality of images are at least a portion of a video, (see Francis paragraph 0013, wherein ... image-based and/or video-based monitoring...), as claimed.
With respect to claim 3, combination of Francis, Qi and Brisebois further discloses wherein the plurality of images comprise a visible light image, an infrared image, an ultraviolet image, a thermal image, a night vision image, or any combination thereof, (see Francis paragraph 0016, wherein ...visual sensors (surveillance cameras), depth sensors, thermal imagers...), as claimed.
With respect to claim 4, combination of Francis, Qi and Brisebois further discloses wherein the plurality of images comprise images from two or more different videos, (see figure 1, numerical 6 multiple cameras), as claimed.
With respect to claim 5, combination of Francis, Qi and Brisebois further discloses wherein the one or more elements comprise a person, a group of people, a vehicle, equipment or a component thereof, or any combination thereof, (see figure 1, numerical 9 a person), as claimed.
With respect to claim 6, combination of Francis, Qi and Brisebois further discloses wherein the at least one sensor comprises an identification scanner, a barrier sensor, an equipment sensor, and any combination thereof, (see figure 3A, numerical 301), as claimed.
With respect to claim 8, combination Francis, Qi and Brisebois disclose all the elements as claimed and rejected in claim 1 above. However, they fail to disclose querying at least one database for at least one database attribute of the at least one element, as claimed.
But, it is well known “official notice” in the art to search “query” a database for attributes of an element, (for example-US Pub. 2017/0074964, Abstract). Therefore, it would have been obvious to one ordinary skilled in the art at the effective date of invention to utilize the well-known knowledge of querying database for an attribute of an element in to the Francis system in as suggested in paragraph 0016 detecting object, thereby yielding the predictable results as claimed.
With respect to claim 9, combination of Francis, Qi and Brisebois further discloses wherein the tracking uses a convolution neural network to analyze analyzes a position of the at least one element through the plurality of images, (see Qi paragraph 0079, wherein ...P3D-Attention network, which combines a P3D convolutional neural network and the attention mechanism, uses the P3D-Attention module to apply attention to the channel and the feature map by using the attention mechanism, and includes the DCAM and the SAM; and 0081, wherein ...The SAM mainly learns location information in a weight matrix...), as claimed.
With respect to claim 10, combination of Francis, Qi and Brisebois further discloses the ascertaining of the state of the environment further integrates the at least one attribute and the spatio-temporal attributes with one or more of (a) at least one database attribute, (b) at least one safety attribute, or (c) at least one relational attribute, (see Francis figure 3A, Metadata), as claimed.
With respect to claim 12, combination of Francis, Qi and Brisebois further discloses the integrated reasoning model incorporates a Bayesian probabilistic model, (see Brisebois paragraph 0019, wherein ...Bayesian reasoning, to evaluate the probability of a hypothesis, a prior probability is initially assigned to the hypothesis. The prior probability may later be updated to a posterior probability in light of new, relevant data. For example, a machine learning algorithm, such as a classifier, may be used to analyze an event log (e.g., based on Bayesian probabilities) to determine a risk...), as claimed.
With respect to claim 13, combination Francis, Qi and Brisebois disclose all the elements as claimed and rejected in claim 1 above. However, they fail to disclose the integrated reasoning model incorporates a data-driven model, as claimed.
But, it is well known “official notice” in the art to search to have data-driven model for reasoning model that is use for prediction using big data (for example-US Pub. 2022/0237345, paragraph 0017). Therefore, it would have been obvious to one ordinary skilled in the art at the effective date of invention to utilize the well-known knowledge of using data-driven model for reasoning models to the Francis system in as suggested in figure 3A, numerical 317, thereby yielding the predictable results as claimed.
Claims 15-20 are rejected for the same reasons as set forth in the rejection for claims 1-6, because claims 15-20 are claiming subject matter of similar scope as claimed in claims 1-6.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Francis et al (US Pub. 2022/0277217) in view of Qi et al (US Pub. 2025/0078516) and Brisebois et al (US Pub. 2017/0329972) as applied to claim 1 above, and further in view of Kobayashi et al (US 11,704,930)
With respect to claim 7, combination of Francis, Qi and Brisebois all the elements as claimed and as disclose in claim 1 above. However, They fail to explicitly disclose wherein the at least one attribute comprises a badge identification number, a vehicle identification number, a person's name, a company associated with the at least one element, a job title of a person, and any combination thereof, as claimed.
Kobayashi teaches wherein the at least one attribute comprises a badge identification number, a vehicle identification number, a person's name, a company associated with the at least one element, a job title of a person, and any combination thereof, (see figure 1B, numerical 51A, "a vehicle identification number"), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of monitoring using image analysis. The teaching of Kobayashi to read a vehicle identification number of a vehicle in order to monitor can be incorporated into Francis system as suggested in paragraph 0016, ...moving object can be detected..., for suggestion, and modifying the system yields a monitoring system (see col. 1, lines 19-21), for motivation.
Conclusion
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/VIKKRAM BALI/ Primary Examiner, Art Unit 2663
