DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1. This action is responsive to communications: Application filed on September 1, 2023, and Drawings filed on September 1, 2023.
2. Claims 1–20 are pending in this case. Claim 1, 11, 20 are independent claims.
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 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.
Response to Arguments
Applicant's arguments filed 12/8/2025 have been fully considered but they are not persuasive.
Applicant argues that:
The Office Action appears to equate the target quality parameter of He to the claimed "compression qualities". See Office Action, pp. 12-13. Even assuming for the sake of argument only that the Office Action's equation is otherwise appropriate or correct, which Applicant does not address, He still would not disclose, teach, or suggest to "identify a type of an application or a service providing at least one image", and to "select a denoising model related to the identified type of the application or the service from a plurality of denoising models trained to correspond to the respective compression qualities", as recited in claim 1. Instead, at best, He determines its target quality parameter based on the internal characteristics of the image data (e.g., data amount, patch analysis), whereas the claimed "compression qualities" are determined using external context information (e.g., the type of the application or the service).
The term “service” is not specifically defined, it is therefore subject to the broadest interpretation reasonable. The prior art specifically teaches the limitation of wherein the system uses a particular type of compression service with regard to the image. The particular type of compression service is tracked based on a quality parameter. This quality parameter is then used to select denoise model. Therefore, the claim language is specifically taught by the prior arts and applicant’s argument is unpersuasive.
Allowable Subject Matter
Claims 4 and 14, 16, 17, 18,19 are 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.
With regard to claim 4 and 14, the prior arts do not disclose the electronic device of claim 3, wherein the processor is further configured to: analyze edge components, based on the two or more areas; and exclude a first area of the two or more areas, in which an edge component has a first value equal to or smaller than a designated threshold, from calculation of the average or median value.
With regard to claim 16 the prior arts do not disclose The method of claim 11, wherein the corrected image is associated with a first configured quality, the method further comprising: providing a user interface enabling interaction with a user in order to identify an intention of the user; receiving a first user input indicating whether the user is satisfied with the corrected image; when the first user input indicates that the user rejects the corrected image: applying a second configured quality preconfigured by another user for the at least one image; and when the first user input indicates that the user approves the corrected image: storing the first configured quality in a memory.
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.
Claim(s) 1, 11, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over He, Patent No.: CN 111091518 B, in view of Mironica, Pub. No.: 20220270209 A1.
With regard to claim 1:
He disclose an electronic device comprising; a memory storing instructions; and a processor operatively connected to and the memory (“the embodiment of the invention further claims an electronic device, comprising a processor and a machine readable storage medium, a machine readable storage medium stored with machine executable instructions executable by the processor, the processor is machine executable instructions cause: realizing the image processing method step provided by the first aspect.”), wherein the instructions, when executed by the processor, cause the electronic device to: identify a type of an application or a service providing at least one image (the type of service is a type of compression services with different compression qualities : “S110, obtaining the image to be processed, using a plurality of quality parameters to compress the image to be processed to obtain a plurality of images to be processed after compression. wherein each compressed image to be processed corresponds to a quality parameter. S120, based on the data amount of the plurality of compressed image to be processed, a plurality of quality parameters and the data amount of the image to be processed, determining the target quality parameter from a plurality of quality parameters.”), select a denoising model related to the identified type of the application or the service from a plurality of denoising models (denoise model is selected based quality parameter: “inputting the to-be-processed image to the preset corresponding to the target quality parameter of the image denoising model, obtaining the image after removing noise, wherein each quality parameter corresponding to a image denoising model, corresponding to the target quality parameter of the image denoising is based on the original image sample and the compressed sample after the original image sample is compressed by the target quality parameter, the preset model obtained by training the image denoising model.”), trained to correspond to the respective compression qualities (“In some other examples, if using the target quality parameter to compress the original image sample to obtain compressed sample, then using the compressed sample and the corresponding original image sample to train the preset of the image denoising model to obtain a corresponding to the target quality parameter of the image denoising then the target quality parameter corresponding to the image denoising model can well adopt the target quality parameter to compress the compressed image to obtain the denoising process.”); perform an image correction based on the denoising model, model (Image is corrected based on the selected denoise model: “An image processing method provided by the embodiment of the invention, firstly obtaining the image to be processed, then using a plurality of quality parameters to compress the image to be processed to obtain a plurality of compressed images to be processed, wherein each compressed image to be processed corresponds to a quality parameter; then based on the data amount of the plurality of compressed image to be processed, a plurality of quality parameters and the data amount of the image to be processed, determining the target quality parameter from the plurality of quality parameters; Thus, it can determine the original image corresponding to the to-be-processed image to compress to obtain the image to be processed by using the quality parameter, then the image to be processed is input to the preset corresponding to the target quality parameter of the image denoising model to obtain the image after denoising. Thus, the noise image can be denoised. because the target quality parameter corresponding to image denoising model is used with the target quality parameter corresponding to the image sample to train, and because the model image denoising the quality parameter of the image to be processed corresponding to the obtained image denoising the obtained model is more suitable for removing noise to the image to be processed, The denoising effect is better.”).
He does to discloses the aspect of display a screen comprising at least one image via the display, and display a corrected image via the display.
However Mironica discloses a display; a memory; and a processor operatively connected to the display and the memory (paragraph 161 to 163: “The computing device 1000 includes a storage device 1006 includes storage for storing data or instructions. As an example, and not by way of limitation, the storage device 1006 includes a non-transitory storage medium described above. The storage device 1006 may include a hard disk drive (HDD), flash memory, a Universal Serial Bus (USB) drive, or a combination of these or other storage devices. As shown, the computing device 1000 includes one or more I/O interfaces 1008, which are provided to allow a user to provide input to (e.g., user strokes), receive output from, and otherwise transfer data to and from the computing device 1000. These I/O interfaces 1008 may include a mouse, keypad, or a keyboard, a touch screen, camera, optical scanner, network interface, modem, another known I/O device, or a combination of these I/O interfaces 1008. The touch screen may be activated with a stylus or a finger. The I/O interfaces 1008 may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers. In certain implementations, I/O interfaces 1008 are configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces and/or any other graphical content as may serve a particular implementation.”) display a screen comprising at least one image via the display (system display the original image in the interface paragraph 110: “Turning now to FIG. 5, a graphical example of the image artifact removal system 106 removing compression artifacts is described. For instance, FIG. 5 illustrates a graphical user interface of editing compressed digital images in accordance with one or more implementations. As shown, FIG. 5 illustrates a client device 500 having a graphical user interface 502 that includes an image 504 (i.e., a digital image). In various implementations, the client device 500 represents the client device 102 introduced above with respect to FIG. 1. As illustrated, the client device 500 includes an image editing application that implements the image editing system 104, which utilizes the image artifact removal system 106. Also, in some implementations, the image artifact removal system 106, or optionally the image editing application, generates the graphical user interface 502 in FIG. 5.”), and display a corrected image via the display (the improved is displayed in the graphical interface paragraph 111: “In various implementations, the image editing application facilitates user interaction with the image 504. For example, the image editing application and/or the image artifact removal system 106 provides an image filer tool 506 (e.g., a JPEG artifact removal tool) that enables the user to request automatically removal of the compression artifacts in the image 504. In response to detecting a compression artifact removal request, the image artifact removal system 106 generates (as described above) and displays an improved image within the graphical user interface 502 (e.g., displayed as a new image layer or replacement image).”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Mironica to He so the user can view the image before compression denoise and how it looks after the denoise operation order to make more informed decision such as changing denoise model based on the result quality.
Claim 11 is rejected for the same reason as claim 1.
Claim 20 is rejected for the same reason as claim 1.
Claims 2, 8, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over He, Patent No.: CN 111091518 B, in view of Mironica, and further in view of Ma, CN 111565258 B
With regard to claims 2 and 12:
He and Mironica disclose the aspect wherein the instructions, when executed by the processor is cause the electronic device to: determine a compression quality of the at least one image (“In some other examples, if using the target quality parameter to compress the original image sample to obtain compressed sample, then using the compressed sample and the corresponding original image sample to train the preset of the image denoising model to obtain a corresponding to the target quality parameter of the image denoising then the target quality parameter corresponding to the image denoising model can well adopt the target quality parameter to compress the compressed image to obtain the denoising process.”); the at least one image is a compressed image obtained by compression with a designated compression quality (He: “The embodiment of the invention claims an image processing method, device, electronic device and storage medium, which can firstly obtain the image to be processed, then using a plurality of quality parameters to compress the image to be processed to obtain a plurality of compressed images to be processed, wherein each compressed image to be processed corresponds to a quality parameter; then based on the data amount of the plurality of compressed image to be processed, a plurality of quality parameters and the data amount of the image to be processed, determining the target quality parameter from the plurality of quality parameters; Thus, it can determine the original image corresponding to the to-be-processed image to compress to obtain the image to be processed by using the quality parameter, then the image to be processed is input to the preset corresponding to the target quality parameter of the image denoising model to obtain the image after denoising. Thus, the noise image can be denoised. because the target quality parameter corresponding to image denoising model is used with the target quality parameter corresponding to the image sample to train, and because the model image denoising the quality parameter of the image to be processed corresponding to the obtained image denoising the obtained model is more suitable for removing noise to the image to be processed, The denoising effect is better. Of course, any product or method of the invention does not necessarily need to reach all the advantages mentioned above.”).
He and Mironica do not disclose the electronic device of claim 1, wherein the processor is further configured to classify the compression quality in units of patches of the at least one image.
However Ma discloses the aspect wherein the processor is further configured to classify the compression quality in units of patches of the at least one image (“FIG. 9 shows an exemplary compression procedure 900 for use in the case where the cross bar ReRAM array is configured using the training procedure of FIG. 5. At block 902, a device input or select input image patch X (e.g., 4 x 4 or 8 x 8 patches obtained from the image to be compressed) is input or selected with a suitable training cross bar ReRAM array. At block 904, the device calculates the forward propulsion by applying the patch to the ReRAM array: A=X* w, wherein A is the output activity vector, and wherein w is a weight array matrix. at the frame 906, the device in the ReRAM array found in the maximum element A (e.g., " winner " output) neuron and recording the neuron (i.e., the specific column in the ReRAM array). at block 908, the compressed patch X_c so as to be represented by A [index] * w [: index], wherein ": " indicates that each row of the column can have different values (and so A is a vector for each row in the column has one entry, so as to provide a value for each pixel of the patch, The patch may be a 4 x 4 matrix). At block 910, the device repeats the operation of frame 902-908 for all other patches of the image to compress the entire image, and then outputs the index list to the host device (e.g., in the form shown in FIG. 8).”) and wherein the at least one image is a compressed image obtained by compression with a designated compression quality (“FIG. 9 shows an exemplary compression procedure 900 for use in the case where the cross bar ReRAM array is configured using the training procedure of FIG. 5. At block 902, a device input or select input image patch X (e.g., 4 x 4 or 8 x 8 patches obtained from the image to be compressed) is input or selected with a suitable training cross bar ReRAM array. At block 904, the device calculates the forward propulsion by applying the patch to the ReRAM array: A=X* w, wherein A is the output activity vector, and wherein w is a weight array matrix. at the frame 906, the device in the ReRAM array found in the maximum element A (e.g., " winner " output) neuron and recording the neuron (i.e., the specific column in the ReRAM array). at block 908, the compressed patch X_c so as to be represented by A [index] * w [: index], wherein ": " indicates that each row of the column can have different values (and so A is a vector for each row in the column has one entry, so as to provide a value for each pixel of the patch, The patch may be a 4 x 4 matrix). At block 910, the device repeats the operation of frame 902-908 for all other patches of the image to compress the entire image, and then outputs the index list to the host device (e.g., in the form shown in FIG. 8).”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Ma to He and Mironica so the user would be able to set the quality of the images based on the number of patches to be used so the user would have more control over the quality of the image after compression to specific needs.
With regard to claim 8:
He and Mironica and Ma disclose The electronic device of claim 2, wherein the instructions, when executed by the processor further cause the electronic device to: during the image correction, provide a user interface, wherein the user interface enables interaction with a user in order to identify information on the image correction and an intention of the user; receive a user input based on the user interface; and perform post-processing of the corrected image, based on the user input (Mironica paragraph 110 to 111: “Turning now to FIG. 5, a graphical example of the image artifact removal system 106 removing compression artifacts is described. For instance, FIG. 5 illustrates a graphical user interface of editing compressed digital images in accordance with one or more implementations. As shown, FIG. 5 illustrates a client device 500 having a graphical user interface 502 that includes an image 504 (i.e., a digital image). In various implementations, the client device 500 represents the client device 102 introduced above with respect to FIG. 1. As illustrated, the client device 500 includes an image editing application that implements the image editing system 104, which utilizes the image artifact removal system 106. Also, in some implementations, the image artifact removal system 106, or optionally the image editing application, generates the graphical user interface 502 in FIG. 5.
In various implementations, the image editing application facilitates user interaction with the image 504. For example, the image editing application and/or the image artifact removal system 106 provides an image filer tool 506 (e.g., a JPEG artifact removal tool) that enables the user to request automatically removal of the compression artifacts in the image 504. In response to detecting a compression artifact removal request, the image artifact removal system 106 generates (as described above) and displays an improved image within the graphical user interface 502 (e.g., displayed as a new image layer or replacement image).”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Mironica to He and Ma so the user can view the image before compression denoise and how it looks after the denoise operation order to make more informed decision such as changing denoise model based on the result quality.
Claim 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over He, Patent No.: CN 111091518 B, in view of Mironica and Ma, and further in view of Uemura et al., Pub. No.: 2020/0302611A1.
With regard to claim 3 and 13:
He and Mironica and Ma not disclose the electronic device of claim 2, wherein the instructions, when executed by the processor cause the electronic device to: extract two or more areas in the units of patches from the at least one image; and classify the compression quality of the at least one image, based on an average or median value of compression qualities of the two or more areas.
However Uemura disclose The electronic device of claim 2, wherein the instructions, when executed by the processor cause the electronic device to: extract two or more areas in the units of patches from the at least one image; and classify the compression quality of the at least one image, based on an average or median value of compression qualities of the two or more areas (paragraph 72: “Moreover, according to the above-described embodiment, the estimation apparatus 1 further divides target data into regions by a given method and calculates the average of the degree of compression obtained from the auxiliary image regarding each divided region. The estimation apparatus 1 couples the divided regions based on the average of each divided region and estimates the coupled regions as the common part According to this configuration, the estimation apparatus 1 may estimate the desired information corresponding to the specific task with high accuracy by using the regions obtained by dividing the target data.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Uemura to He and Mironica and Ma so the system can more accurately determine the quality of image compression by sampling multiple regions of an image.
Claim 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over He, Patent No.: CN 111091518 B, in view of Mironica and Ma, and further in view of HONKALA, WO 2020128134 A1.
With regard to claim 6:
He and Mironica and Ma disclose the electronic device of claim 2, wherein the instructions, when executed by the processor cause the electronic device to: store, in the memory, multiple denoising models previously trained for each of various compression qualities; select the denoising model trained to correspond to classification of the compression quality of the at least one image from among the multiple denoising models (He: “In some other examples, before processing the image to be processed, the embodiment of the invention further can preset a plurality of image denoising model, each of image denoising model can be corresponding to a quality parameter, so, it can determine the target quality parameter, The image to be processed can be input to the image denoising corresponding to the target quality parameter.”).
He and Mironica and Ma do not disclose the aspect wherein during selection of the denoising model, select the denoising model by additionally considering at least one of a user's personalization, a type of a service or application providing an image, and/or a screen size of the display.
However Honkala discloses the aspect wherein during selection of the denoising model, select the denoising model by additionally considering at least one of a user's personalization, a type of a service or application providing an image, and/or a screen size of the display (the denoising model is selected based on best performance for a specific type of data/service: “In step 613, the computing device may train the noise model using the ML training network configured in step 609 and based on the data samples collected in step 61 1. The computing device may use a GAN framework for training the noise model, and may train the noise model (as the generator of the GAN) and the discriminator of the GAN jointly and in turn. The computing device may use suitable techniques used for GAN training (e.g., backpropagation, stochastic gradient descent (SGD), etc.) to train the noise model. More details regarding training various types of noise models are further discussed in connection with FIGS. 7-9. If the noise type of the plurality of noisy data samples is not determined (step 607: N), the method may proceed to step 615. For example, the noise type of the plurality of noisy data samples might not be determined if there is no information (e.g., no record in the database) indicating the noise type corresponding to the data sample type and/or the sensor type of the plurality of noisy data samples. In step 615, the computing device may train one or more noise models corresponding to one or more types of noise. For example, the computing device may train a noise model for additive noise, a noise model for multiplicative noise, and a noise model for signal dependent noise. In step 617, the computing device may select, from the one or more trained noise models, a noise model to be used for training the denoising model 301. The selection may be performed based on the performance of each trained noise model. Additionally or alternatively, the computing device may train a denoising model based on and corresponding to each trained noise model, and may select, from the trained denoising models, a denoising model with the best performance. A performance metric that may be used to evaluate and/or select trained noise models and/or trained denoising models may be based on known characteristics of the data expected to be output by the models. Additionally or alternatively, the evaluation and/or selection may be a semi-automatic process based on quality ratings from users.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Honkala to He and Mironica and Ma so the system can more accurately determine the type of denoise model to used based on the type of service that is required.
Claim 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over He, Patent No.: CN 111091518 B, in view of Mironica, and further in view of NEMOTO, 20100073510 A1.
With regard to claim 7:
He and Mironica and Ma disclose the electronic device of claim 2, wherein the instructions, when executed by the processor cause the electronic device to: remove compression artifacts from the at least one image according to a correction strength corresponding to the denoising model (He: “de-noising module, for inputting the to-be-processed image to the preset corresponding to the target quality parameter of the image denoising model, obtaining the image after removing noise, wherein each quality parameter corresponding to a image denoising model, corresponding to the target quality parameter of the image denoising is based on the original image sample and the compressed sample after the original image sample is compressed by the target quality parameter, the preset model obtained by training the image denoising model.”).
He and Mironica and Ma do not disclose the aspect of reconstruct the at least one image to an original image before compression.
However Nemoto discloses the aspect of reconstruct the at least one image to an original image before compression (paragraph 2010: “The image processing unit 35 further converts the three-primary-color image signal subjected to processes such as white balance adjustment and gamma correction into an image signal according to an image encoding technique for still images or moving images, such as a luminance signal or a color-difference signal, to perform a compression expansion process. The image processing unit 35 outputs a resulting compression encoded signal GDa of a still image or moving image to a recording/playback unit 37. The image processing unit 35 further performs a process of outputting an image signal of a still image or moving image that is not subjected to a compression expansion process and/or a compression encoded signal to an external device (not shown). When a compression encoded signal GDb is supplied from the recording/playback unit 37, the image processing unit 35 performs a process of returning the compression encoded signal GDb into an original image signal before the compression encoding process. The image processing unit 35 further generates a display image signal HG from the image signal DVa, and supplies the display image signal HG to a display unit 38. Further, in response to a display signal HE supplied from the control unit 51, the image processing unit 35 performs processes such as generating a display image signal HG that provides on-screen display of information or the like indicating that a presentation-image imaging mode has been set.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Nemoto to He and Mironica and Ma so the system would be able to return the image to its original form before compression to provide the user with the best result with a lossless quality image.
Claim 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over He, Patent No.: CN 111091518 B, in view of Mironica and Ma, and further in view of VIACHESLAV, JP 2018107797 A.
With regard to claim 9:
He and Mironica and Ma disclose the aspect of provide the corrected image obtained by removing compression artifacts from the at least one image, the removing being performed based on the denoising model trained according to the compression quality (Mironica paragraph 110: “Turning now to FIG. 5, a graphical example of the image artifact removal system 106 removing compression artifacts is described. For instance, FIG. 5 illustrates a graphical user interface of editing compressed digital images in accordance with one or more implementations. As shown, FIG. 5 illustrates a client device 500 having a graphical user interface 502 that includes an image 504 (i.e., a digital image). In various implementations, the client device 500 represents the client device 102 introduced above with respect to FIG. 1. As illustrated, the client device 500 includes an image editing application that implements the image editing system 104, which utilizes the image artifact removal system 106. Also, in some implementations, the image artifact removal system 106, or optionally the image editing application, generates the graphical user interface 502 in FIG. 5.”), and display a corrected image via the display (paragraph 111: “In various implementations, the image editing application facilitates user interaction with the image 504. For example, the image editing application and/or the image artifact removal system 106 provides an image filer tool 506 (e.g., a JPEG artifact removal tool) that enables the user to request automatically removal of the compression artifacts in the image 504. In response to detecting a compression artifact removal request, the image artifact removal system 106 generates (as described above) and displays an improved image within the graphical user interface 502 (e.g., displayed as a new image layer or replacement image).”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Mironica to He and Ma so the user can view the image before compression denoise and how it looks after the denoise operation order to make more informed decision such as changing denoise model based on the result quality.
He and Mironica and Ma do not disclose the aspect during displaying of a screen, temporarily download the at least one image from an external device corresponding to the at least one image, based on content execution.
However VIACHESLAV disclose the aspect wherein the processor is further configured to: during displaying of a screen, temporarily download the at least one image from an external device corresponding to the at least one image, based on content execution (paragraph 17: “Internal components of an example encoder 100 for use with the method of encoding image data according to the examples described herein are schematically illustrated in FIG. The encoder 100 of FIG. 1 is arranged to receive the image data 102. Image data 102 represents an image, which may be the entire image or the entire image, or a portion, part, or subset of a larger image. The image may be, for example, an image from a web page accessed by a browser of a computing device, such as a smartphone browser; an image captured by an image capture device, such as a camera, of a computing device; or in a storage area of the computing device An image to be downloaded or stored. The image may include any graphic or visual content such as text, graphics, pictures and / or photos. The image may be a still image or a moving image. For example, the image data may be video image data.”); and provide the corrected image obtained by removing compression artifacts from the at least one image, the removing being performed based on the denoising model trained according to the compression quality (paragraph 27: “By applying an appropriate filter to the decoded image data, compression artifacts in the decoded image data can be removed. Such filters can therefore be used to reduce compression artifacts in the image, such as blocking, eg visible blocks, or ringing, eg visible rings. In an example, bilateral filters and / or anisotropic filters can be applied to the decoded image data, for example, to reduce blocking, ringing, or both of these artifacts.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply VIACHESLAV to He and Mironica and Ma so the system can denoise compressed image from the internet allowing smaller sized image to be download saving system resources.
Claim 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over He, Patent No.: CN 111091518 B, in view of Mironica and Ma, and further in view of KIM, US 20160205049 A1
With regard to claim 10:
He and Mironica and Ma disclose the electronic device of claim 2, wherein the instructions, when executed by the processor cause the electronic device to: display a corresponding screen based on content execution comprising the at least one image, and during displaying of the user-selected image, classify the compression quality of the user-selected image and provide a result (MIronica, paragraph 110 and 111 “Turning now to FIG. 5, a graphical example of the image artifact removal system 106 removing compression artifacts is described. For instance, FIG. 5 illustrates a graphical user interface of editing compressed digital images in accordance with one or more implementations. As shown, FIG. 5 illustrates a client device 500 having a graphical user interface 502 that includes an image 504 (i.e., a digital image). In various implementations, the client device 500 represents the client device 102 introduced above with respect to FIG. 1. As illustrated, the client device 500 includes an image editing application that implements the image editing system 104, which utilizes the image artifact removal system 106. Also, in some implementations, the image artifact removal system 106, or optionally the image editing application, generates the graphical user interface 502 in FIG. 5.
In various implementations, the image editing application facilitates user interaction with the image 504. For example, the image editing application and/or the image artifact removal system 106 provides an image filer tool 506 (e.g., a JPEG artifact removal tool) that enables the user to request automatically removal of the compression artifacts in the image 504. In response to detecting a compression artifact removal request, the image artifact removal system 106 generates (as described above) and displays an improved image within the graphical user interface 502 (e.g., displayed as a new image layer or replacement image).”) It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Mironica to He and Ma so the user can view the image before compression denoise and how it looks after the denoise operation order to make more informed decision such as changing denoise model based on the result quality.
He and Mironica and Ma do not disclose the aspect of based on a user's image selection on the display, enlarge a user-selected image at a certain ratio and provide the same; provide a result thereof via a pop-up message.
However Kim teaches the aspect of based on a user's image selection on the display, enlarge a user-selected image at a certain ratio and provide the same (paragraph 199: “After a prescribed thumbnail 2601 has been selected from the image thumbnail list, if an input 100nn for dragging the selected thumbnail 2601 to a chat window is received (FIG. 26 (b)), the controller 180 can enlarge and display an image 2602 corresponding to the selected thumbnail 2601 (FIG. 26 (c)).”); provide a result thereof via a pop-up message (paragraph 209: “Referring to FIG. 29 (c), the re-search result may be output as a popup window 2901. If a touch 10y to the re-search result is applied and a drag 10z to the execution screen of the message application is then applied, referring to FIG. 29 (d), the controller 180 can input the re-search result to the text input window 801.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Kim to He and Mironica and Ma so the user can view the image more clearly after selection and can based notified of the completion of denoising and se the result.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/DI XIAO/Primary Examiner, Art Unit 2178