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 .
Status of Claims
This communication is in response to the Application Filed on 05/30/2024.
Claims 1-20 are pending in this application.
Drawings
The drawings fled on 05/30/2024 are accepted by the Examiner.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/28/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Claim Objections
Claims 1 and 12 are objected to because of the following informalities:
Claim 1 states “the target product.” Perhaps it should say “the target software application” as they indicate the same entity (see Applicant Specification ¶ [0037]). A single term should be used or explanation of different terms indicating the same entity should be provided.
Claim 12 states “the training source product.’ Perhaps it should say “the target source software application” as they indicate the same entity (see Applicant Specification ¶ [0035]]. A single term should be used or explanation of different terms indicating the same entity should be provided.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a module” in claim 10
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Claim 10: ‘a module’ corresponds to Fig. 11. “Next, the style attributes of the target images (act 1120). This can be done using a video or image analyzer, as mentioned above,” Applicant Specification ¶ [00112].
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
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 non obviousness.
Claims 1-4, 7-11, 13-14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Laflamme et al. (US 11232623 B2, hereinafter, “Laflamme”) in view of Lee et al. (US 20240054602 A1, hereinafter, “Lee”), and further in view of Moran et al. (Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps, 2020, hereinafter, “Moran”).
Regarding claim 1, Laflamme discloses a method for generating training data for training a super-resolution model that is configured to transform images from a first resolution to a second resolution, (See Laflamme, Col. 3, lines 54-63, the trained neural network learns to generate a high-definition image when provided with a lower-quality (e.g., lower resolution) input signal. In operation (e.g., in runtime on a user device and as described with respect to FIG. 3), an application (e.g., software application) such as a video game can generate simple low-resolution images and use the trained neural network (e.g., trained with the method 200 described with respect to FIG. 2A) to convert the low-resolution images into high-resolution versions in real-time. Examiner considers low resolution to be first resolution and high resolution to be second resolution) the method comprising:
identifying a target software application (See Laflamme, Col. 4, lines 49-50, an application 114 (e.g., a video game, a simulation, or other software application)) that is used during runtime to generate target images at a first resolution (See Laflamme, Col. 3, lines 56-60, In operation (e.g., in runtime on a user device and as described with respect to FIG. 3), an application (e.g., software application) such as a video game can generate simple low-resolution images. Examiner considers target images to be low resolution images and first resolution to be low resolution) and for which the super-resolution model is to be trained to transform the target images from the first resolution to corresponding images at the second resolution (See Laflamme, Col. 3, lines 59-63, generate simple low-resolution images and use the trained neural network (e.g., trained with the method 200 described with respect to FIG. 2A) to convert the low-resolution images into high-resolution versions in real-time),
the target software application (See Laflamme, Col. 4, lines 49-50, an application 114 (e.g., a video game, a simulation, or other software application)) being integrated with a first image generator (See Laflamme, Col. 8, lines 15-16, the application 114 generates (e.g., via the game engine 104) that generates the target images for the target product at the first resolution during runtime of the target software application (See Laflamme, Col. 3, lines 59-63, generate simple low-resolution images and use the trained neural network (e.g., trained with the method 200 described with respect to FIG. 2A) to convert the low-resolution images into high-resolution versions in real-time);
[identifying style attributes of the target images;]
[evaluating a plurality of sample products] to identify a training source software application (See Laflamme, Col. 4, lines 49-50, an application 114 (e.g., a video game, a simulation, or other software application)) that is configured for use by a second image generator (See Laflamme, Col. 8, lines 15-16, the application 114 generates (e.g., via the game engine 104) to generate output images at the first resolution (See Laflamme, Col. 3, lines 59-63, generate simple low-resolution images and use the trained neural network (e.g., trained with the method 200 described with respect to FIG. 2A) to convert the low-resolution images into high-resolution versions in real-time. Examiner considers target images to be the same as output images) [with style attributes that are similar to the style attributes of the target software application;]
causing the second image generator (See Laflamme, Col. 8, lines 15-16, the application 114 generates (e.g., via the game engine 104) to generate (i) the output images at the first resolution (See Laflamme, Col. 3, lines 59-63, generate simple low-resolution images and use the trained neural network (e.g., trained with the method 200 described with respect to FIG. 2A) to convert the low-resolution images into high-resolution versions in real-time. Examiner considers target images to be the same as output images) as well as (ii) correlated output images having a second resolution that is different than the first resolution of the output images (See Laflamme, Col. 5, lines 62-65, The low-resolution data map and the high-resolution data map are linked and represent the same ‘view’ (e.g., a same virtual camera frustum view) of the environment at different resolutions and with the same data type); and
generating training data for the super-resolution model by pairing the output images having the first resolution with the correlated output images having the second resolution (See Laflamme, Col. 5, lines 57-63, a pair of data maps includes one high-resolution data map (e.g., a high-resolution RGB image) and one low-resolution data map of the same type of data (e.g., a low-resolution RGB image) taken with the same position, orientation and conditions).
However, Laflamme was not been relied upon to teach identifying style attributes of the target images; and
evaluating a plurality of sample products to identify a [training source software application that is configured for use by a second image generator to generate output images at the first resolution] with style attributes that are similar to the style attributes of the target software application.
Lee teaches identifying style attributes of the target images (See Lee, ¶ [0143] comparing the information related to the first image 115 with the information about the image provided from the application. Examiner considers image related information as style attributes); and
[evaluating a plurality of sample products to identify a training source software application that is configured for use by a second image generator to generate output images at the first resolution] with style attributes that are similar to the style attributes of the target software application (See Lee, ¶ [0145] The AI setter 238 may compare information related to the images provided from the applications with information related to the first image 115 to determine an application through which the first image 115 is similar to an image provided, or to predict an application from which the image is provided).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Laflamme’s reference to identify style attributes of the target images and generate output images at the first resolution with style attributes that are similar to the style attributes of the target software application based on the method of Lee’s reference. The suggestion/motivation would have been to maximize image quality by processing images based on artificial intelligence as suggested by Lee at ¶ [0006].
However, the combination of Laflamme and Lee was not relied upon to teach evaluating a plurality of sample products [to identify a training source software application that is configured for use by a second image generator to generate output images at the first resolution with style attributes that are similar to the style attributes of the target software application.]
Moran teaches evaluating a plurality of sample products (See Moran, Pg. 196, right col., lines 1-3, GUI information already present in existing apps (specifically screenshots and GUI metadata) acquired via mining software repositories (MSR)); Pg. 200, left col., section 2.2.2, lines 4-8, mining and automatically executing the top-250 Android apps in each category of Google Play excluding game categories, resulting in 14,382 unique screens and 191,300 labeled GUI components (after data-cleaning)).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Laflamme and Lee’s references to evaluate a plurality of sample products based on the method of Moran’s reference. The suggestion/motivation would have been to train an accurate neural network as suggested by Moran at Pg. 200, left col., section 2,.2,.2, line 1).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lee and Moran with Laflamme to obtain the invention as specified in claim 1.
Regarding claim 2, the combination of Laflamme, Lee, and Moran teach the method of claim 1, the method further comprising: applying the super-resolution model to the training data to generate a trained super-resolution model (See Laflamme, Col. 6, lines 45-50, the neural network lossy rendering module 116 uses the plurality of pairs of data maps in the dataset to train a neural network to associate low-resolution data maps with high-resolution data maps of the same view of the environment such that the neural network learns to estimate a high-resolution data map when given a low-resolution data map).
Regarding claim 3, the combination of Laflamme, Lee, and Moran teach the method of claim 1, the method further comprising: modifying the second image generator to generate (i) the output images at the first resolution (See Laflamme, Col. 3, lines 59-63, generate simple low-resolution images and use the trained neural network (e.g., trained with the method 200 described with respect to FIG. 2A) to convert the low-resolution images into high-resolution versions in real-time. Examiner considers target images to be the same as output images) as well as (ii) correlated output images having a second resolution that is different than the first resolution of the output images (See Laflamme, Col. 5, lines 62-65, The low-resolution data map and the high-resolution data map are linked and represent the same ‘view’ (e.g., a same virtual camera frustum view) of the environment at different resolutions and with the same data type. Examiner considers the use of the trained neural network as modifying the second image generator).
Regarding claim 4, the combination of Laflamme, Lee, and Moran teach the method of claim 1, wherein the rendering of the high-resolution images occurs locally to a computing system that performs the method (See Laflamme, Col. 5, lines 10-12, The game engine 104 and the neural network lossy renderer 116 may be integrated directly within the application 114; Col. 8, lines 5-11, FIG. 3 is a method 300 for generating high-resolution images during an execution of an application 114 (e.g., at runtime) on a user device 102. During the method 300, the application may be executing on the device 102 (e.g., the CPU 103 may be retrieving instructions from the application 114 stored in the memory 101, and executing such instructions)).
Regarding claim 7, the combination of Laflamme, Lee, and Moran teach the method of claim 1, wherein the target software application comprises a video game (See Laflamme, Col. 3, lines 58-59, an application (e.g., software application) such as a video game) and the first image generator comprises a gaming engine that generates the target images during runtime of the game (See Laflamme, Col. 8, lines 15-16, the application 114 generates (e.g., via the game engine 104); Col. 3, lines 56-60, In operation (e.g., in runtime on a user device and as described with respect to FIG. 3), an application (e.g., software application) such as a video game can generate simple low-resolution images).
Regarding claim 8, the combination of Laflamme, Lee, and Moran teach the method of claim 1, wherein the style attributes include at least one of: color, texture, size, or font of text of the target images (See Laflamme, Col. 5, lines 44-47, A data map can include the following types of data: an RGB image, a normal map, a depth map, a reflectivity map, a motion vector map, and any combination thereof; Col. 11, lines 10-13, As seen in FIG. 6B, the neural network lossy renderer system 100 generates details of the terrain including terrain shape, lighting, shading, color (e.g., not shown in FIG. 6B)).
Regarding claim 9, the combination of Laflamme, Lee, and Moran teach the method of claim 1, wherein the style attributes include one or more of: a framerate, a type of anti-aliasing, shading, lighting, physically-based rendering (PBR), dynamic range, depth of field, motion blur, ambient occlusion, or color grading (See Laflamme, Col. 5, lines 44-47, A data map can include the following types of data: an RGB image, a normal map, a depth map, a reflectivity map, a motion vector map, and any combination thereof; Col. 11, lines 10-13, As seen in FIG. 6B, the neural network lossy renderer system 100 generates details of the terrain including terrain shape, lighting, shading, color (e.g., not shown in FIG. 6B)).
Regarding claim 10, the combination of Laflamme, Lee, and Moran teach the method of claim 1, wherein the style attributes of the target image are identified with a module configured to examine metadata declarations that identify the style attributes (See Lee, ¶ [0145] The AI setter 238 may compare information related to the images provided from the applications with information related to the first image 115 to determine an application through which the first image 115 is similar to an image provided, or to predict an application from which the image is provided).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Laflamme and Moran’s reference wherein the style attributes of the target image are identified with a module configured to examine metadata declarations that identify the style attributes based on the method of Lee’s reference. The suggestion/motivation would have been to maximize image quality by processing images based on artificial intelligence as suggested by Lee at ¶ [0006].
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lee with the combination of Laflamme and Moran to obtain the invention as specified in claim 10.
Regarding claim 11, the combination of Laflamme, Lee, and Moran teach the method of claim 1, wherein the style attributes of the target image are identified with an image or video analyzer configured to identify style attributes of images and/or videos (See Lee, ¶ [0145] The AI setter 238 may compare information related to the images provided from the applications with information related to the first image 115 to determine an application through which the first image 115 is similar to an image provided, or to predict an application from which the image is provided. Examiner considers the module in claim 10 and the image analyzer as being similar).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Laflamme and Moran’s reference wherein the style attributes of the target image are identified with an image or video analyzer configured to identify style attributes of images and/or videos based on the method of Lee’s reference. The suggestion/motivation would have been to maximize image quality by processing images based on artificial intelligence as suggested by Lee at ¶ [0006].
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lee with the combination of Laflamme and Moran to obtain the invention as specified in claim 11.
Regarding claim 13, the combination of Laflamme, Lee, and Moran teach the method of claim 1, wherein the second resolution is a higher-resolution than the first resolution (See Laflamme, Col. 3, lines 24-26, A high-resolution output data map of the data type is generated from the low-resolution data map. Examiner considers a first resolution to the low resolution and second resolution to be high resolution).
Regarding claim 14, the combination of Laflamme, Lee, and Moran teach the method of claim 1, wherein the method further includes modifying the second image generator to generate multiple correlated data sets at different resolutions (See Laflamme, Col. 6, lines 34-36, operation 204 of the method 200 is carried out a plurality of times to generate a large dataset of pairs of data maps; Col. 7, lines 59-61, the looping within operation 204 is performed so that a plurality of data map pairs are generated ; Col. 5, lines 62-65, The low-resolution data map and the high-resolution data map are linked and represent the same ‘view’ (e.g., a same virtual camera frustum view) of the environment at different resolutions and with the same data type).
Regarding claim 17, Laflamme discloses a computing system comprising: a hardware processing system comprising a hardware processor; and one or more storage devices storing executable instructions that are executed by the hardware processing system for causing the computing system to perform operations comprising (See Laflamme, Col. 4, lines 2-6, The CPU 103 is any type of processor, processor assembly comprising multiple processing elements (not shown), having access to a memory 101 to retrieve instructions stored thereon, and execute such instructions):
identifying a target software application (See Laflamme, Col. 4, lines 49-50, an application 114 (e.g., a video game, a simulation, or other software application)) that is used during runtime to generate target images at a first resolution (See Laflamme, Col. 3, lines 56-60, In operation (e.g., in runtime on a user device and as described with respect to FIG. 3), an application (e.g., software application) such as a video game can generate simple low-resolution images. Examiner considers target images to be low resolution images and first resolution to be low resolution) and for which the super-resolution model is to be trained to transform the target images from the first resolution to corresponding images at the second resolution (See Laflamme, Col. 3, lines 59-63, generate simple low-resolution images and use the trained neural network (e.g., trained with the method 200 described with respect to FIG. 2A) to convert the low-resolution images into high-resolution versions in real-time),
the target software application (See Laflamme, Col. 4, lines 49-50, an application 114 (e.g., a video game, a simulation, or other software application)) being integrated with a first image generator (See Laflamme, Col. 8, lines 15-16, the application 114 generates (e.g., via the game engine 104) that generates the target images for the target product at the first resolution during runtime of the target software application (See Laflamme, Col. 3, lines 59-63, generate simple low-resolution images and use the trained neural network (e.g., trained with the method 200 described with respect to FIG. 2A) to convert the low-resolution images into high-resolution versions in real-time);
[identifying style attributes of the target images;]
[evaluating a plurality of sample products] to identify a training source software application (See Laflamme, Col. 4, lines 49-50, an application 114 (e.g., a video game, a simulation, or other software application)) that is configured for use by a second image generator (See Laflamme, Col. 8, lines 15-16, the application 114 generates (e.g., via the game engine 104) to generate output images at the first resolution (See Laflamme, Col. 3, lines 59-63, generate simple low-resolution images and use the trained neural network (e.g., trained with the method 200 described with respect to FIG. 2A) to convert the low-resolution images into high-resolution versions in real-time. Examiner considers target images to be the same as output images) [with style attributes that are similar to the style attributes of the target software application;]
modifying the second image generator (See Laflamme, Col. 8, lines 15-16, the application 114 generates (e.g., via the game engine 104; Col. 3, lines 59-63, generate simple low-resolution images and use the trained neural network (e.g., trained with the method 200 described with respect to FIG. 2A) to convert the low-resolution images into high-resolution versions in real-time. Examiner considers target images to be the same as output images) to generate (i) the output images at the first resolution (See Laflamme, Col. 3, lines 59-63, generate simple low-resolution images and use the trained neural network (e.g., trained with the method 200 described with respect to FIG. 2A) to convert the low-resolution images into high-resolution versions in real-time. Examiner considers target images to be the same as output images) as well as (ii) correlated output images having a second resolution that is different than the first resolution of the output images (See Laflamme, Col. 5, lines 62-65, The low-resolution data map and the high-resolution data map are linked and represent the same ‘view’ (e.g., a same virtual camera frustum view) of the environment at different resolutions and with the same data type); and
generating training data for the super-resolution model by pairing the output images having the first resolution with the correlated output images having the second resolution (See Laflamme, Col. 5, lines 57-63, a pair of data maps includes one high-resolution data map (e.g., a high-resolution RGB image) and one low-resolution data map of the same type of data (e.g., a low-resolution RGB image) taken with the same position, orientation and conditions).
However, Laflamme was not relied upon to teach identifying style attributes of the target images; and
evaluating a plurality of sample products to identify a [training source software application that is configured for use by a second image generator to generate output images at the first resolution] with style attributes that are similar to the style attributes of the target software application.
Lee teaches identifying style attributes of the target images (See Lee, ¶ [0143] comparing the information related to the first image 115 with the information about the image provided from the application. Examiner considers image related information as style attributes); and
[evaluating a plurality of sample products to identify a training source software application that is configured for use by a second image generator to generate output images at the first resolution] with style attributes that are similar to the style attributes of the target software application (See Lee, ¶ [0145] The AI setter 238 may compare information related to the images provided from the applications with information related to the first image 115 to determine an application through which the first image 115 is similar to an image provided, or to predict an application from which the image is provided).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Laflamme’s reference to identify style attributes of the target images and generate output images at the first resolution with style attributes that are similar to the style attributes of the target software application based on the method of Lee’s reference. The suggestion/motivation would have been to maximize image quality by processing images based on artificial intelligence as suggested by Lee at ¶ [0006].
However, the combination of Laflamme and Lee was not relied upon to teach evaluating a plurality of sample products [to identify a training source software application that is configured for use by a second image generator to generate output images at the first resolution with style attributes that are similar to the style attributes of the target software application.]
Moran teaches evaluating a plurality of sample products (See Moran, Pg. 196, right col., lines 1-3, GUI information already present in existing apps (specifically screenshots and GUI metadata) acquired via mining software repositories (MSR)); Pg. 200, left col., section 2.2.2, lines 4-8, mining and automatically executing the top-250 Android apps in each category of Google Play excluding game categories, resulting in 14,382 unique screens and 191,300 labeled GUI components (after data-cleaning)).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Laflamme and Lee’s reference to evaluate a plurality of sample products based on the method of Moran’s reference. The suggestion/motivation would have been to train an accurate neural network as suggested by Moran at Pg. 200, left col., section 2,.2,.2, line 1).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lee and Moran with Laflamme to obtain the invention as specified in claim 17.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Laflamme et al. (US 11232623 B2, hereinafter, “Laflamme”) in view of Lee et al. (US 20240054602 A1, hereinafter, “Lee”), and further in view of Moran et al. (Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps, 2020, hereinafter, “Moran”), and further in view of Yannakakis et al. (US 20200298118 A1, hereinafter, “Yannakakis”).
Regarding claim 5, the combination of Laflamme, Lee, and Moran was not relied upon to teach wherein the training source software application comprises a demo for the target software application, the demo comprising a version of the software application that is executable without an integrated game engine and that comprises part of but not all of the software application.
Yannakakis teaches wherein the training source software application comprises a demo for the target software application the demo comprising a version of the software application that is executable without an integrated game engine and that comprises part of but not all of the software application (See Yannakakis, ¶ [0109] for example, alpha testing, beta testing and/or soft launch and leading to the generation of an improved game for hard launch in step 314. Examiner considers the beta testing as a demo for the software application).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Laflamme, Lee, and Moran’s reference wherein the training source software application comprises a demo for the target software application, the demo comprising a version of the software application that is executable without an integrated game engine and that comprises part of but not all of the software application based on the method of Yannakakis’s reference. The suggestion/motivation would have been for improvements to both the technology of game application development and the technology of gaming applications themselves as suggested by Yannakakis at ¶ [0022].
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Yannakakis with the combination of Laflamme, Lee, and Moran to obtain the invention as specified in claim 5.
Regarding claim 6, the combination of Laflamme, Lee, and Moran was not relied upon to teach wherein the training source software application comprises a video originating from a source other than the first image generator.
Yannakakis teaches wherein the training source software application comprises a video originating from a source other than the first image generator (See Yannakakis, ¶ [0024] one or more gaming applications 248, one or more gaming bots 250; ¶ [0027] one or more versions of the gaming application 248 can be stored including, for example, multiple versions or updates of the gaming application. Examiner considers the multiple versions of the gaming application as originating from another source, as they have different information from the training source software application).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Laflamme, Lee, and Moran’s reference wherein the training source software application comprises a video originating from a source other than the first image generator based on the method of Yannakakis’s reference. The suggestion/motivation would have been for improvements to both the technology of game application development and the technology of gaming applications themselves as suggested by Yannakakis at ¶ [0022].
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Yannakakis with the combination of Laflamme, Lee, and Moran to obtain the invention as specified in claim 6.
Claims 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Laflamme et al. (US 11232623 B2, hereinafter, “Laflamme”) in view of Lee et al. (US 20240054602 A1, hereinafter, “Lee”), and further in view of Moran et al. (Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps, 2020, hereinafter, “Moran”), and further in view of Silvestri (What’s Different in FC 24 Nintendo Switch, 2024, hereinafter, “Silvestri”).
Regarding claim 12, the combination of Laflamme, Lee, and Moran was not relied upon to teach wherein causing the second image generator to generate (i) the output images at the first resolution as well as (ii) the correlated output images having the second resolution includes causing the second image generator to utilize multiple viewports when rendering content from the training source product, each viewport rendering at a different resolution.
Silvestri teaches wherein causing the second image generator to generate (i) the output images at the first resolution as well as (ii) the correlated output images having the second resolution includes causing the second image generator to utilize multiple viewports when rendering content from the training source product, each viewport rendering at a different resolution (See Silvestri, Figure at top of website. Examiner considers the different depths in the image to be multiple viewports rendering at different resolutions).
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Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Laflamme, Lee, and Moran’s reference wherein causing the second image generator to generate (i) the output images at the first resolution as well as (ii) the correlated output images having the second resolution includes causing the second image generator to utilize multiple viewports when rendering content from the training source product, each viewport rendering at a different resolution based on the method of Silvestri’s reference. The suggestion/motivation would have been to enhance animation as suggested by Silvestri in section “A Standout Football Simulation for Nintendo Switch.”
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Silvestri with the combination of Laflamme, Lee, and Moran to obtain the invention as specified in claim 12.
Regarding claim 18, the combination of Laflamme, Lee, and Moran was not relied upon to teach wherein modifying the second image generator to generate (i) the output images at the first resolution as well as (ii) the correlated output images having the second resolution includes modifying the second image generator to utilize multiple viewports when rendering content from the training source software application, each viewport rendering at a different resolution.
Silvestri teaches wherein modifying the second image generator to generate (i) the output images at the first resolution as well as (ii) the correlated output images having the second resolution includes modifying the second image generator to utilize multiple viewports when rendering content from the training source software application, each viewport rendering at a different resolution (See Silvestri, Figure at top of website. Examiner considers the different depths in the image to be multiple viewports rendering at different resolutions).
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Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Laflamme, Lee, and Moran’s reference wherein causing the second image generator to generate (i) the output images at the first resolution as well as (ii) the correlated output images having the second resolution includes causing the second image generator to utilize multiple viewports when rendering content from the training source product, each viewport rendering at a different resolution based on the method of Silvestri’s reference. The suggestion/motivation would have been to enhance animation as suggested by Silvestri in section “A Standout Football Simulation for Nintendo Switch.”
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Silvestri with the combination of Laflamme, Lee, and Moran to obtain the invention as specified in claim 18.
Allowable Subject Matter
Claims 15-16 and 19-20 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.
Claims 15 and 16 contain subject matter that is not disclosed or made obvious in the cited art.
In regard to claim 15, when considering claim 15 as a whole, prior art of record fails to disclose or render obvious, alone, or in combination:
“The method of claim 1, further comprising:
training the super-resolution model with the training data to generate a trained super-resolution model; and
applying the trained super-resolution model to images of a different target software application to generate new high-resolution images and performing a regression analysis on the trained super-resolution model for regression relative to performance of the super-resolution model and the trained super-resolution for generating the new high-resolution images for the different target software application.”
In regard to claim 16, when considering claim 16 as a whole, prior art of record fails to disclose or render obvious, alone, or in combination:
“The method of claim 15, wherein the method further includes either persisting or, alternatively, reverting changes made to the super-resolution model when generating the trained super-resolution model, wherein the method includes persisting the changes when it is determined regression to the super-resolution model relative to the different target product has not exceeded a regression threshold and the method alternatively includes reverting the changes when it is determined regression to the super-resolution model has exceeded the regression threshold.”
In regard to claim 19, when considering claim 15 as a whole, prior art of record fails to disclose or render obvious, alone, or in combination:
“The computing system of claim 18, further comprising:
applying the super-resolution model to the training data to generate a trained super-resolution model; and
applying the trained super-resolution model to images of a different target software application to generate new high-resolution images and performing a regression analysis on the trained super-resolution model for regression relative to performance of the super-resolution model and the trained super-resolution for generating the new high-resolution images for the different target product.”
In regard to claim 20, when considering claim 15 as a whole, prior art of record fails to disclose or render obvious, alone, or in combination:
“The computing system of claim 19, wherein the method further includes either persisting or, alternatively, reverting changes made to the super-resolution model when generating the trained super-resolution model, wherein the method includes persisting the changes when it is determined regression to the super-resolution model relative to the different target product has not exceeded a regression threshold and the method alternatively includes reverting the changes when it is determined regression to the super-resolution model has exceeded the regression threshold.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hou et al. (WO 2024076394 A1) discloses a method to train and generate high quality images from low quality images which are then used to train the model.
Xiao et al. (Neural Super sampling for Real-time Rendering, 2020) discloses a neural network used for image super sampling to improve learned super resolution models
Elmoznino et al. (WO 2021092686 A1) discloses a method for training a model using unpaired image datasets to generate paired datasets, which are then used as ground truth for the model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jasmin Marcelino Hernandez whose telephone number is (571) 270-0211. The examiner can normally be reached 7am-3pm EST.
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/JASMIN MARCELINO HERNAND/Examiner, Art Unit 2676
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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