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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lamego (U.S. PG-PUB NO. 2023/0186636) in view of Kulandai (U.S. PG-PUB NO. 2022/0405939).
-Regarding claim 1, Kulandai discloses an end-to-end enterprise computer vision system (FIG. 1) comprising: a memory (memory, paragraph 87); and a processor that is configured to execute machine readable instructions stored in the memory for causing the processor (processor, paragraph 87) to: a software application performing one or more computer vision functions (tenant applications (or solutions) 146, paragraph 30); and process analytics from the one or more computing vision functions, wherein the analytics provide real-time insight associated with operations of an enterprise (AI metric generation system 132 then uses artificial intelligence and machine learning modules to perform further perception analytics on the events detected by AI image/video processing system 130, paragraph 28).
Lamego is silent to teaching that developing. However, the claimed limitation is well known in the art as evidenced by Kulandai.
In the same field of endeavor, Kulandai teaches developing (generating a second edge model based upon training the first edge model using the plurality of annotated images, paragraph 52).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Lamego with the teaching of Kulandai in order to improve system accuracy.
-Regarding claim 2, the combination further discloses the software application performs one or more computer vision functions on image data or video data related to the enterprise received in real-time (Lamego, images or video streams, paragraph 24).
-Regarding claim 3, the combination further discloses the computer vision functions comprise one or more of: facial detection, object detection, object tracking, and object counting (Kulandai, detect objects within the video data 106, paragraph 23).
-Regarding claim 4, the combination further discloses the software application is deployed to an edge device (Kulandai, edge mode, paragraph 52).
-Regarding claim 5, the combination further discloses the processor that is configured to execute machine readable instructions stored in the memory further causes the processor to: collect image data and video data from one or more deployment locations associated with the enterprise (Lemego, edge computing system 110 and cameras 112-114 are deployed at a physical location or facility 111, paragraph 23); annotate the collected image data and video data to generate a training dataset (Kulandai, annotating, paragraph 28); train an Artificial Intelligence (AI)-based model using the training dataset (model training component 132, paragraph 34); deploy the software application to the edge device; and execute the deployed software application (Kulandai, edge device 110 may employ an edge model 114 to determine object information 116 within the video data 106, paragraph 21), wherein executing the deployed software application comprises performing run-time inference on the image data or video data related to the enterprise received in real-time (Lamego, AI image/video processing system 130 can receive the video streams or images from cameras 112-114, paragraph 28).
-Regarding claim 6, the combination further discloses training the Al-based model is performed on a cloud resource (Lamego, cloud computing, paragraph 92).
-Regarding claim 7, the combination further discloses performing run-time inference is based on the trained Al-based model (Kulandai, generating a second edge model based upon training the first edge model using the plurality of annotated images, paragraph 52).
-Regarding claim 8, Lamego discloses an end-to-end enterprise computer vision method (FIG. 1), comprising: a software application performing one or more computer vision functions (tenant applications (or solutions) 146, paragraph 30); and processing analytics from the one or more computing vision functions, wherein the analytics provide real-time insight associated with operations of an enterprise (AI metric generation system 132 then uses artificial intelligence and machine learning modules to perform further perception analytics on the events detected by AI image/video processing system 130, paragraph 28).
Lamego is silent to teaching that developing. However, the claimed limitation is well known in the art as evidenced by Kulandai.
In the same field of endeavor, Kulandai teaches developing (generating a second edge model based upon training the first edge model using the plurality of annotated images, paragraph 52).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Lamego with the teaching of Kulandai in order to improve system accuracy.
-Regarding claim 9, the combination further discloses collecting image data and video data from one or more deployment locations associated with the enterprise (Lemego, edge computing system 110 and cameras 112-114 are deployed at a physical location or facility 111, paragraph 23); annotating the collected image data and video data to generate a training dataset (Kulandai, annotating, paragraph 28); training an Artificial Intelligence (AI)-based model using the training dataset (model training component 132, paragraph 34); deploying the software application to an edge device; and executing the deployed software application (Kulandai, edge device 110 may employ an edge model 114 to determine object information 116 within the video data 106, paragraph 21), wherein executing the deployed software application comprises performing run-time inference on the image data or video data related to the enterprise received in real-time (Lamego, AI image/video processing system 130 can receive the video streams or images from cameras 112-114, paragraph 28).
-Regarding claim 10, the combination further discloses performing run-time inference is based on the trained Al-based model (Kulandai, generating a second edge model based upon training the first edge model using the plurality of annotated images, paragraph 52).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PING Y HSIEH whose telephone number is (571)270-3011. The examiner can normally be reached Monday-Friday, 9am-4pm.
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/PING Y HSIEH/ Primary Examiner, Art Unit 2664
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