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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/16/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Allowable Subject Matter
Claim 3-5,8,12-14 and 18-20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2,6-7,9-11 and 15-17 is/are rejected under 35 U.S.C. 102(a) as being taught by Godwin et al. (US Patent Number 11232330-B2, hereinafter “Godwin”).
Regarding claim 1, Godwin teaches: An electronic device comprising: memory storing one or more instructions and a first neural network model;
a communication interface; and at least one processor operatively coupled with the memory and the communication interface, wherein the one or more instructions, when executed by the at least one processor, causes the electronic device to: (Fig. 2)
identify a scene type of a first frame included in a content by inputting the first frame into the first neural network model, ([0040], " Since neural networks need extensive training to produce specific results, by analyzing the input data to identify various parameters and objects allows the electronic device to select a particular neural network to achieve a desired result."; [0080], "In block 506 the electronic device analyzes the transformed image in order to generate metadata. In certain embodiments, the analysis of the transformed image is performed by machine learning. In certain embodiments, the machine learning is unsupervised. The generated metadata statistically describes the received remote sensed data. In certain embodiments, the analysis of the transform image is performed by a machine learning engine, similar to the machine learning engine 330 of FIG. 3. The analysis of the transformed image provides a prediction as to the content in the image.")
control the communication interface to transmit the scene type of the first frame to a server, ([0036], "Remote sensing data can be received from one or more information repositories, servers, databases, or directly from an aerial vehicle such as a satellite (similar to satellite 116 of FIG. 1) a drone, an aircraft and the like. The remote sensing data can include metadata that can indicate the capturing source of the data, the geographical location of the data, resolution, sensor type, and the like.")
receive, from the server through the communication interface in response to the transmitted scene type of the first frame, a second neural network model and a first parameter corresponding to the scene type of the first frame,
replace the first neural network model with the second neural network model, ([0080], "In block 508 the electronic device selects a particular neural network to perform a second analysis of the received remote sensed data. In certain embodiments, the selection of a particular neural network is based on the generated metadata from block 506. The selecting of a particular neural network can be based on various metadata received with the remote sensed data, transform parameters, machine learning parameters, as well as analytics.")
and perform image processing on the first frame based on the first parameter, and wherein the second neural network model is one of a plurality of second neural network models respectively corresponding to a plurality of scene types that can be output from the first neural network model. ([0040], " Since neural networks need extensive training to produce specific results, by analyzing the input data to identify various parameters and objects allows the electronic device to select a particular neural network to achieve a desired result."; [0081], "In block 510 the electronic device performs a second analysis by the selected neural network of block 508. The second analysis is performed to extract data from the received remote sensed data. The second analysis can include loss data that is domain specialized. In certain embodiments, the loss data is based on prior approaches to semantic segmentation.")
Regarding claim 2, Godwin teaches: The electronic device of claim 1, wherein the one or more instructions, when executed by the at least one processor, further cause the electronic device to: identify a scene type of a second frame after the first frame by inputting the second frame into the second neural network model, (Fig. 4A; [0040], " Since neural networks need extensive training to produce specific results, by analyzing the input data to identify various parameters and objects allows the electronic device to select a particular neural network to achieve a desired result."; [0080], "In block 506 the electronic device analyzes the transformed image in order to generate metadata. In certain embodiments, the analysis of the transformed image is performed by machine learning. In certain embodiments, the machine learning is unsupervised. The generated metadata statistically describes the received remote sensed data. In certain embodiments, the analysis of the transform image is performed by a machine learning engine, similar to the machine learning engine 330 of FIG. 3. The analysis of the transformed image provides a prediction as to the content in the image.")
control the communication interface to transmit the scene type of the second frame to the server, (Fig. 4A; [0036], "Remote sensing data can be received from one or more information repositories, servers, databases, or directly from an aerial vehicle such as a satellite (similar to satellite 116 of FIG. 1) a drone, an aircraft and the like. The remote sensing data can include metadata that can indicate the capturing source of the data, the geographical location of the data, resolution, sensor type, and the like.")
receive, from the server through the communication interface in response to the transmitted scene type of the second frame, a third neural network model and a second parameter corresponding to the scene type of the second frame from the server through the communication interface, replace the second neural network model with the third neural network model, (Fig. 4A; [0080], "In block 508 the electronic device selects a particular neural network to perform a second analysis of the received remote sensed data. In certain embodiments, the selection of a particular neural network is based on the generated metadata from block 506. The selecting of a particular neural network can be based on various metadata received with the remote sensed data, transform parameters, machine learning parameters, as well as analytics.")
and perform image processing on the second frame based on the second parameter, and wherein the third neural network model is one of a plurality of third neural network models respectively corresponding to a plurality of scene types that can be output from the second neural network model. (Fig. 4A; [0083], "In certain embodiments, the extract data can be compared to the generated metadata from block 506. Based on the comparison, the results can be input into the selected neural network to improve the training of the neural network. For example, particular neural network was selected (block 508) based in part on the predicted results of the first analysis (block 506). If the predictions from block 506 are not accurate, the selected neural network can be trained to accommodate the inaccurate prediction to improve the results generated by the selected neural network. Similarly, if the predictions from block 506 are accurate, then the selected neural network can be trained to improve the results as well as trained to skip portions of the processing to increase speed and efficiency.")
Regarding claim 6, Godwin teaches: The electronic device of The electronic device of wherein the memory further stores a plurality of image processing engines, and wherein the one or more instructions, when executed by the at least one processor, further cause the electronic device to:
perform image processing on the frames included in the content by using one or more image processing engines from the plurality of image processing engines corresponding to the scene types of the frames included in the content. ([0040], " Since neural networks need extensive training to produce specific results, by analyzing the input data to identify various parameters and objects allows the electronic device to select a particular neural network to achieve a desired result."; [0081], "In block 510 the electronic device performs a second analysis by the selected neural network of block 508. The second analysis is performed to extract data from the received remote sensed data. The second analysis can include loss data that is domain specialized. In certain embodiments, the loss data is based on prior approaches to semantic segmentation.")
Regarding claim 7, Godwin teaches: The electronic device of claim 1, wherein the one or more instructions, when executed by the at least one processor, further causes the electronic device to: update the scene types of the frames included in the content by a predetermined interval. ([0040], " Since neural networks need extensive training to produce specific results, by analyzing the input data to identify various parameters and objects allows the electronic device to select a particular neural network to achieve a desired result."; [0081], "In block 510 the electronic device performs a second analysis by the selected neural network of block 508. The second analysis is performed to extract data from the received remote sensed data. The second analysis can include loss data that is domain specialized. In certain embodiments, the loss data is based on prior approaches to semantic segmentation.")
Regarding claim 9, Godwin teaches: The electronic device of claim 1, further comprising: a display, and wherein the one or more instructions, when executed by the at least one processor, further causes the electronic device to: control the display to display the first frame that went through image processing. ([0033], "The processor 240 is also coupled to the input 250 and the display 255. The operator of the electronic device 200 can use the input 250 to enter data or inputs, or a combination thereof, into the electronic device 200. Input 250 can be a keyboard, touch screen, mouse, track ball or other device capable of acting as a user interface to allow a user in interact with electronic device 200. For example, the input 250 can include a touch panel, a (digital) pen sensor, a key, an ultrasonic input device, or an inertial motion sensor. The touch panel can recognize, for example, a touch input in at least one scheme along with a capacitive scheme, a pressure sensitive scheme, an infrared scheme, or an ultrasonic scheme. In the capacitive scheme, the input 250 is able to recognize a touch or proximity. Input 250 can be associated with sensor(s) 265, a camera, or a microphone, such as or similar to microphone 220, by providing additional input to processor 240. In certain embodiments, sensor 265 includes inertial sensors (such as, accelerometers, gyroscope, and magnetometer), optical sensors, motion sensors, cameras, pressure sensors, heart rate sensors, altimeter, and the like.")
Regarding claim 10, claim 10 has been analyzed with regard to claim 1 and is rejected for the same reasons of obviousness as used above.
Regarding claim 11, claim 11 has been analyzed with regard to claim 2 and is rejected for the same reasons of obviousness as used above.
Regarding claim 15, claim 15 has been analyzed with regard to claim 6 and is rejected for the same reasons of obviousness as used above.
Regarding claim 16, claim 16 has been analyzed with regard to claim 1 and is rejected for the same reasons of obviousness as used above as well as in accordance with Godwin further teaching on: A non-transitory computer readable medium, having instructions stored therein, which when executed by a processor in an electronic device, cause the electronic device to perform a method comprising (Fig. 2)
Regarding claim 17, claim 17 has been analyzed with regard to claim 2 and is rejected for the same reasons of obviousness as used above as well as in accordance with Godwin further teaching on: A non-transitory computer readable medium, having instructions stored therein, which when executed by a processor in an electronic device, cause the electronic device to perform a method comprising (Fig. 2)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jinsu Hwang whose telephone number is (703)756-1370. The examiner can normally be reached Mon -Thu 10am-8am EST.
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/JINSU HWANG/Examiner, Art Unit 2667 /MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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