DETAILED ACTION
The present application, filed on 2/22/2024 is being examined under the AIA first inventor to file provisions.
As a result of Applicant’s pre-appeal brief from 10/22/2025, the prosecution is herewith reopened. The following is a non-final Office Action.
Overall, Claims 2-21 are pending and have been considered below.
Claim Rejections - 35 USC § 101
35 USC 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 2-21 are rejected under 35 USC 101 because the claimed invention is not directed to patent eligible subject matter. The claimed matter is directed to a judicial exception, i.e. an abstract idea, not integrated into a practical application, and without significantly more.
Per Step 1 of the multi-step eligibility analysis, claims 2-8 are directed to a computer implemented method, claims 9-15 are directed to a system, and claims 16-21 are directed to computer executable instructions stored on a non-transitory storage medium.
Thus, on its face, each independent claim and the associated dependent claims are directed to a statutory category of invention.
[INDEPENDENT CLAIMS]
Per Step 2A.1. Independent claim 2, (which is representative of independent claims 9, 16) is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 2 (which is representative of independent claims 9, 16) recite an abstract idea, shown in bold below:
[A] A method comprising:
[B] training, by one or more processors, a plurality of machine learning models trained to evaluate visual aspects of images and output an image quality of the images based on the evaluated visual aspects;
[C] deploying, by a hardware accelerator comprising a plurality of compute tiles, the plurality of machine learning models;
[D] programmatically combining different content assets among a set of content assets, including images, in different ways to create multiple different digital components using the set of content assets;
[E] evaluating a quality of each digital component among the multiple different digital components using one or more of the plurality of machine learning models (i) deployed by the hardware accelerator and (ii) trained to evaluate visual aspects of the images in the digital component;
[F] replacing, based on the evaluating, one or more content assets that were combined to create a given digital component with one or more other content assets resulting in a visually altered digital component; and
[G] distributing the visually altered digital component to multiple different client devices across a communication network.
Independent claim 2 (which is representative of independent claims 9, 16) recites: combining images ([D]); evaluating the quality of the digital components of the image ([E]); replacing the poor-quality components ([F]); and distributed the quality enhanced image ([G]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: “evaluating and improving the visual quality of collated digital images”.
This is a combination that, under its broadest reasonable interpretation, covers reasonable performance of limitations expressing observation, opinion in the human mind. Nothing in the claim elements precludes the steps from being practically performed in the human mind. These limitations fall under the Mental Processes, i.e., Concepts Performed in the Human Mind grouping of abstract ideas (see MPEP 2106.04(a)(2)).
Accordingly, it is concluded that independent claim 2 (which is representative of independent claims 9, 16) recites an abstract idea that represents a judicial exception.
[INDEPENDENT CLAIMS – QUALIFIERS]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the added elements “by one or more processor,” and “by a hardware accelerator” recite computing elements at a high level of generality, generally linking the use of a judicial exception to a particular technological environment (see MPEP 2106.05(h)), or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Further, utilizing tools like “machine learning models”, are nothing more than general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These qualifiers of the independent claims do not preclude from carrying out the identified abstract idea “evaluating and improving the visual quality of collated digital images”, and do not serve to integrate the identified abstract idea into a practical application.
[INDEPENDENT CLAIMS – ADDITIONAL STEPS]
The additional steps in the independent claims, shown not bolded above, recite: training a machine-learning model ([B]), deploying a hardware accelerator ([C]). When considered individually, they amount to nothing more than generally linking the use of the judicial exception to particular technological environment or field of use. Thus, it is reasonable to conclude that these claim elements do not integrate the identified abstract idea (“evaluating and improving the visual quality of collated digital images”) into a practical application (see MPEP 2106.05(h)).
Therefore, the additional steps of independent claim 2 (which is representative of independent claims 9, 16) do not integrate the identified abstract idea into a practical application and the claims remain a judicial exception.
Per Step 2B. Independent claim 2 (which is representative of claims independent 9, 16) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claims 2, 9, 16 are deemed ineligible.
[DEPENDENT CLAIMS]
Dependent claim 3, which is representative of dependent claims 10, 17, recites:
[A] evaluating a combination two or more images included in the given digital component,
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: “evaluating and improving the visual quality of collated digital images”. The elements in this dependent claim are cover reasonable performance of limitations expressing observation, evaluation, judgement, opinion in the human mind. Nothing in the claim elements precludes the steps from being practically performed in the human mind. These limitations fall under the Mental Processes, i.e., Concepts Performed in the Human Mind grouping of abstract ideas (see MPEP 2106.04(a)(2)).
The dependent claim elements have the same relationship to the underlying abstract idea (“evaluating and improving the visual quality of collated digital images”) as outlined in the independent claims analysis above. Thus, it is readily apparent that the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (“evaluating and improving the visual quality of collated digital images”).
Therefore, dependent claim 3 (which is representative of dependent claims 10, 17) is deemed ineligible.
Dependent claim 4, which is representative of dependent claims 11, 18, recites:
[A] receiving a modification of one of the two or more images;
[B] evaluating the one of the two or more images as modified according to the modification;
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: “evaluating and improving the visual quality of collated digital images”. The elements in this dependent claim are cover reasonable performance of limitations expressing observation, evaluation, judgement, opinion in the human mind. Nothing in the claim elements precludes the steps from being practically performed in the human mind. These limitations fall under the Mental Processes, i.e., Concepts Performed in the Human Mind grouping of abstract ideas (see MPEP 2106.04(a)(2)).
The dependent claim elements have the same relationship to the underlying abstract idea (“evaluating and improving the visual quality of collated digital images”) as outlined in the independent claims analysis above. Thus, it is readily apparent that the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (“evaluating and improving the visual quality of collated digital images”).
Therefore, dependent claim 4 (which is representative of dependent claims 11, 18) is deemed ineligible.
Dependent claim 5, which is representative of dependent claims 12, 19, recites:
[A] evaluating the given digital component with one or more preset quality heuristics;
[B] determining that the given digital component does not comply with the preset quality heuristics based on the evaluating;
[C] generating, in response to determining that the given digital component does not comply with the preset heuristics, one or more recommendations for improving the digital component; and
[D] updating a graphical user interface of a first computing device to present the one or more recommendations.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: “evaluating and improving the visual quality of collated digital images”. The elements in this dependent claim are cover reasonable performance of limitations expressing observation, evaluation, judgement, opinion in the human mind. Nothing in the claim elements precludes the steps from being practically performed in the human mind. These limitations fall under the Mental Processes, i.e., Concepts Performed in the Human Mind grouping of abstract ideas (see MPEP 2106.04(a)(2)).
The dependent claim elements have the same relationship to the underlying abstract idea (“evaluating and improving the visual quality of collated digital images”) as outlined in the independent claims analysis above. Thus, it is readily apparent that the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (“evaluating and improving the visual quality of collated digital images”).
Therefore, dependent claim 5 (which is representative of dependent claims 12, 19) is deemed ineligible.
Dependent claim 7, which is representative of dependent claims 14, 21, recites:
[A] deploying the plurality of machine learning models on the given image to generate a score;
[B] assigning a weight to each score to generate weighted scores;
[C] combining the weighted scores to generate a combined score for the given image; and
[D] comparing the combined score to one or more thresholds.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: “evaluating and improving the visual quality of collated digital images”. The elements in this dependent claim are comparable to receiving data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is reasonable to conclude that these claim elements do not integrate the identified abstract idea (“evaluating and improving the visual quality of collated digital images”) into a practical application (see MPEP 2106.05(f)(2)).
The dependent claim elements have the same relationship to the underlying abstract idea (“evaluating and improving the visual quality of collated digital images”) as outlined in the independent claims analysis above. Thus, it is readily apparent that the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (“evaluating and improving the visual quality of collated digital images”).
Therefore, dependent claim 7 (which is representative of dependent claims 14, 21) is deemed ineligible.
Dependent claim 8, which is representative of dependent claims 15, recites:
[A] determining, for each given image, a total possible score;
[B] computing, for each given image, a ratio of the combined score to the total possible score;
[C] calculating an average of the ratios of each given image among the set of content assets,
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: “evaluating and improving the visual quality of collated digital images”. The elements in this dependent claim are comparable to receiving data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is reasonable to conclude that these claim elements do not integrate the identified abstract idea (“evaluating and improving the visual quality of collated digital images”) into a practical application (see MPEP 2106.05(f)(2)).
The dependent claim elements have the same relationship to the underlying abstract idea (“evaluating and improving the visual quality of collated digital images”) as outlined in the independent claims analysis above. Thus, it is readily apparent that the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (“evaluating and improving the visual quality of collated digital images”).
Therefore, dependent claim 8 (which is representative of dependent claims 15) is deemed ineligible.
Dependent claims 6, which are representative of dependent claims 13, 20, respectively, recite:
wherein the one or more recommendations comprise a first recommendation for modifying one or more visual characteristics of an image included in the given digital component.
These further elements in the dependent claims do not perform any claimed method steps. They describe the nature, structure and/or content of other claim elements – the recommendations – and as such, cannot change the nature of the identified abstract idea (“evaluating and improving the visual quality of collated digital images”), from a judicial exception into eligible subject matter, because they do not represent significantly more (see MPEP 2106.07). The nature, form or structure of the other claim elements themselves do not practically or significantly alter how the identified abstract idea would be performed and do not provide more than a general link to a technological environment.
Therefore, dependent claims 6 (which are representative of dependent claims 13, 20, respectively) are deemed ineligible.
When the dependent claims are considered as a whole, as an ordered combination, the claim elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense. The most significant elements, which form the abstract concept, are set forth in the independent claims. The fact that the computing devices and the dependent claims are facilitating the abstract concept is not enough to confer statutory subject matter eligibility, since their individual and combined significance do not transform the identified abstract concept at the core of the claimed invention into eligible subject matter. Therefore, it is concluded that the dependent claims of the instant application, considered individually, or as a as a whole, as an ordered combination, do not amount to significantly more (see MPEP 2106.07(a)II).
In sum, Claims 2-21 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 difference 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 the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
i. Determining the scope and contents of the prior art.
ii. Ascertaining the differences between the prior art and the claims at issue.
iii. Resolving the level of ordinary skill in the pertinent art.
iv. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2-21 are rejected under 35 U.S.C. 103 as being unpatentable over Smith (US 2019/0080347), in view of Bhat et al (US 2017/0178058), in further view of Zhang (US 2004/0141659).
Regarding Claims 2, 9, 16: Smith discloses: A method comprising:
training, by one or more processors, a plurality of machine learning models trained to evaluate visual aspects of images and output an image quality of the images based on the evaluated visual aspects; {see at least fig2, [0040]-[0053] training a machine learning model}
deploying, by a hardware accelerator comprising a plurality of compute tiles, the plurality of machine learning models; {see at least fig1, rc107, [0038] performance predictor uses machine learning. Smith does not explicitly disclose “hardware accelerator.” However, the difference between the instant application and the prior art is only found in the non-functional descriptive material and is not functionally involved in the recited steps. The steps of the claim would be performed the same regardless of the descriptive material since none of the steps explicitly interact therewith. Limitations that are not functionally interrelated with the useful acts, structure, or properties of the claimed invention carry little or no patentable weight. Thus, this descriptive material will not further limit the scope of the claim and does not distinguish the claimed invention from the prior art in terms of patentability, see In re Ngai, 70 USPQ2d 1862 (CAFC 2004); In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994). Therefore, it would also have been obvious to a person of ordinary skill in the art at filing time use a hardware accelerator, because such data does not functionally relate to the steps in the method claimed and because the subjective interpretation of the hardware accelerator does not patentably distinguish the claimed invention.}
… using one or more of the plurality of machine learning models (i) deployed by the hardware accelerator and (ii) trained to evaluate visual aspects of the images in the digital component; {see at least fig1, rc107, [0038] performance predictor (reads on evaluating images) uses machine-learning model. Smith does not explicitly disclose “hardware accelerator.” However, the difference between the instant application and the prior art is only found in the non-functional descriptive material and is not functionally involved in the recited steps. The steps of the claim would be performed the same regardless of the descriptive material since none of the steps explicitly interact therewith. Limitations that are not functionally interrelated with the useful acts, structure, or properties of the claimed invention carry little or no patentable weight. Thus, this descriptive material will not further limit the scope of the claim and does not distinguish the claimed invention from the prior art in terms of patentability, see In re Ngai, 70 USPQ2d 1862 (CAFC 2004); In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994). Therefore, it would also have been obvious to a person of ordinary skill in the art at filing time use a hardware accelerator, because such data does not functionally relate to the steps in the method claimed and because the subjective interpretation of the hardware accelerator does not patentably distinguish the claimed invention.}
replacing, based on the evaluation, one or more content assets that were combined to create a given digital component with one or more other content assets resulting in a visually altered digital component; and {see at least {0020] replacement value that improves the asset score; [0028] replacement to improve …; [0098]-[0102]; [0116]}
distributing the visually altered digital component to multiple different client devices across a communication network. {see at least [0016] improved digital asset; [0020] transform the asset into improved asset}
Smith does not disclose; however, Bhat discloses:
evaluating each digital component among the multiple different digital components … {see at least [0005] computing a quality score; fig2, rc211, [0034] quality score generation engine}
determining, by the one or more processors, that the quality of a given digital component fails to meet a specified quality heuristic based, at least in part, on an output of the one or more of the plurality of machine learning models; {see at least fig3, rc306, rc308, rc312, [0054] quality levels; fig6, rc602-rc612, [0057] computing (reads on evaluating) quality levels}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Smith to include the elements of Bhat. One would have been motivated to do so, in order to improve the visual quality of digital content. In the instant case, Smith evidently discloses using machine learning to improve visual quality. Bhat is merely relied upon to illustrate the functionality of determining quality analytics in the same or similar context. Since both using machine learning to improve visual quality, as well as determining quality analytics are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Smith, as well as Bhat would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Smith / Bhat.
Smith, Bhat does not disclose, however, Zhang discloses:
programmatically combining different content assets among a set of content assets, including images, in different visual configuration resulting in multiple different digital components based on the set of content assets; {see at least [0003] Many remote sensing applications require images with both high spectral resolution and high spatial resolution. The multispectral images provide high spectral resolution, but low spatial resolution. On the other hand, the panchromatic images provide high spatial resolution, but low spectral resolution. Thus, methods for effectively fusing (combining) the multispectral and panchromatic images to produce fused high spatial resolution (also called pan-sharpened) color images are important. As a result, many image fusion methods have been developed, such as IHS (Intensity, Hue, Saturation), PCA (Principal Components Analysis), wavelet based fusion and SVR (Synthetic Variable Ratio). Among existing methods, the IHS and PCA fusion approaches have been the most popular ones.}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Smith, Bhat to include the elements of Zhang. One would have been motivated to do so, in order to test the quality improvement operation. Zhang is merely relied upon to illustrate the functionality of combining different images in the same or similar context. Since both improving the visual quality of digital content, as well as combining different images are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Smith, Bhat, as well as Zhang would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Smith, Bhat / Zhang.
Regarding Claims 3, 10, 17: Smith, Bhat, Zhang discloses the limitations of Claims 2, 9, 16. Bhat further discloses: further comprising
evaluating a combination of two or more images included in the given digital component, {see at least fig6, rc606, [0057]-[0058] blurriness score for input image and a threshold area (reads on aggregate quality of two images)}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Smith, Bhat to include additional elements of Bhat. One would have been motivated to do so, in order to further improve the visual quality of digital content. In the instant case, Smith, Bhat evidently discloses improving the visual quality of digital content. Bhat is merely relied upon to illustrate the additional functionality of determining quality analytics in the same or similar context. Since the subject matter is merely a combination of old elements, and in the combination each element would have performed the same function it performed separately, one having ordinary skill in the art before the effective filing date would have recognized that the results of the combination were predictable.
Regarding Claims 4, 11, 18: Smith, Bhat, Zhang discloses the limitations of Claims 3, 10, 17. Smith further discloses: further comprising:
receiving a modification of one of the two or more images; {see at least fig5A-fig5E, [0085] modifying images … attributes likely to perform best}
evaluating the one of the two or more images as modified according to the modification; {see at least [0016] assessing the likely performance of assets (reads on evaluating image quality}
Regarding Claims 5, 12, 19: Smith, Bhat, Zhang discloses the limitations of Claims 4, 11, 18. Smith further discloses: further comprising:
generating, in response to determining that the given digital component does not comply with the one or more preset heuristics, one or more recommendations for improving the given digital component; and {see at least [0005] recommended improvements; fig3C, rc316, [0068]-[0070] recommendations}
updating a graphical user interface of a first computing device to present the one or more recommendations. {see at least [0005] recommended improvements; fig3C, rc316, [0068]-[0070] recommendations}
Bhat further discloses:
evaluating the given digital component with one or more preset heuristics; {see at least [0039] image blurry if … high frequencies satisfy a threshold}
determining that the given digital component does not comply with the one or more preset heuristics based on the evaluating; {see at least [0040] high or low variance … blurry or non-blurry image}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Smith, Bhat, Zhang to include additional elements of Bhat. One would have been motivated to do so, in order to further improve the visual quality of digital content. In the instant case, Smith, Bhat, Zhang evidently discloses improving the visual quality of digital content. Bhat is merely relied upon to illustrate the additional functionality of comparing the quality indicator and determining it is not in compliance in the same or similar context. Since the subject matter is merely a combination of old elements, and in the combination each element would have performed the same function it performed separately, one having ordinary skill in the art before the effective filing date would have recognized that the results of the combination were predictable.
Regarding Claims 6, 13, 20: Smith, Bhat, Zhang discloses the limitations of Claims 5, 12, 19. Smith further discloses:
wherein the one or more recommendations comprise a first recommendation for modifying one or more visual characteristics of an image included in the given digital component. {see at least [0005] recommended improvements; fig3C, rc316, [0068]-[0070] recommendations; [0016] providing recommendations to improve the assets score}
Regarding Claims 7, 14, 21: Smith, Bhat, Zhang discloses the limitations of Claims 2, 9, 16. Smith further discloses: further comprising: for each given image among the set of content assets:
deploying the plurality of machine learning models on the given image to generate a score for each quality characteristic of a plurality of quality characteristics; {see at least [abstract] machine learning to generate attribute score}
assigning a weight to each score to generate weighted scores; {see at least [0058] greater weight … lower weight; [0061] higher weight … lower weight}
combining the weighted scores to generate a combined score for the given image; and {see at least [0029] asset scores in combination}
comparing the combined score to one or more thresholds. {see at least [0071] score above or below threshold (reads on comparing with threshold)}
Regarding Claims 8, 15: Smith, Bhat, Zhang discloses the limitations of Claims 2, 14. Smith further discloses: further comprising:
determining, for each given image, a total possible score; {see at least fig1, rc102, [0059] combing attribute scores into a total attribute score; [0029] scoring on first scale (e.g. 0 to 10), or a second scale (e.g., 0 to 100)}
Bhat further discloses:
computing, for each given image, a ratio of the combined score to the total possible score; {see at least [claim3] using a ratio between the area (reads on total possible score) and a threshold area (reads on combined scores)}
calculating an average of the ratios of each given image among the set of content assets; and {see at least [0055] the quality of the images may be an average of other statistical measures}
:
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Smith, Bhat, Zhang to include additional elements of Bhat. One would have been motivated to do so, in order to further improve the visual quality of digital content. In the instant case, Smith, Bhat, Zhang evidently discloses improving the visual quality of digital content. Bhat is merely relied upon to illustrate the additional functionality of determining quality analytics in the same or similar context. Since the subject matter is merely a combination of old elements, and in the combination each element would have performed the same function it performed separately, one having ordinary skill in the art before the effective filing date would have recognized that the results of the combination were predictable.
The prior art made of record and not relied upon which, however, is considered pertinent to applicant's disclosure:
US 20190005043 A1 Hemani; Mayur et al. Automated Digital Asset Tagging using Multiple Vocabulary Sets - Automated digital asset tagging techniques and systems are described that support use of multiple vocabulary sets. In one example, a plurality of digital assets are obtained having first-vocabulary tags Zhangen from a first-vocabulary set. Second-vocabulary tags Zhangen from a second-vocabulary set are assigned to the plurality of digital assets through machine learning. A determination is made that at least one first-vocabulary tag includes a plurality of visual classes based on the assignment of at least one second-vocabulary tag. Digital assets are collected from the plurality of digital assets that correspond to one visual class of the plurality of visual classes. The model is generated using machine learning based on the collected digital assets.
US 20190128666 A1 Lau; Kam Chiu et al. SYSTEMS AND METHODS FOR GATHERING DATA AND INFORMATION ON SURFACE CHARACTERISTICS OF AN OBJECT - A system is provided for gathering information and data on the surface characteristics of an object includes a projector, a table, a first camera and a second camera. The projector is suspended above the table and arranged to project a random pattern of optical indicators onto the table. The optical indicators can be dots, lines, or other such indicators. The table is arranged to hold the object to be inspected. The first camera is positioned above and to one side of the table and angled toward the table. The second camera is positioned above and to opposite side of the table and angled toward the table. The first and second cameras are arranged to capture images of the optical indicators projected onto the object. The system is further arranged to gather information and data from the captured images and determine the surface characteristics of the object from said gathered information and data.
US 20170097948 A1 KERR; BERNARD JAMES et al. SEARCHING USING SPECIFIC ATTRIBUTES FOUND IN IMAGES - In various implementations, specific attributes found in images can be used in a visual-based search. Utilizing machine learning, deep neural networks, and other computer vision techniques, attributes of images, such as color, composition, font, style, and texture can be extracted from a given image. A user can then select a specific attribute from a sample image the user is searching for and the search can be refined to focus on that specific attribute from the sample image. In some embodiments, the search includes specific attributes from more than one image.
US 20080012856 A1 Yu; Daphne et al. Perception-based quality metrics for volume rendering - Perception-based visual quality metrics are used in volume rendering. A perception-based visual quality metric is measured from one or more three-dimensional representations. For example, people tend to notice edges, so a numeric value representing the noticeable edges is calculated. The perception-based metric is used for developing volume renderers, calibrating across different renderers, calibrating across different rendering platforms, determining rendering parameter values as a function of rendering speed, selecting rendering parameter values for a given situation, providing a range of rendering options associated with gradual perception changes, and/or combinations thereof. The perception-based visual quality metric provides a quantifiable representation of importance to the user for a given application, assisting optimization of volume rendering.
US 20130069258 A1 Ballet; Jerome et al. PRODUCTION OF A TRANSPARENT OPTICAL COMPONENT HAVING A CELLULAR STRUCTURE - The invention relates to a transparent optical component having a cellular structure, comprising a network of walls (106), that forms a set of cells (104) that are juxtaposed parallel to a component surface. In order to produce such a component, an irregular set of points (101, 105) in the surface of the component is determined, each point being used to form a centre of one of the cells. A position and an orientation of each wall are then determined such that the set of cells forms a Voronoi partition of the surface of the component. The component has a level of transparency that is compatible with an optical or ophthalmological use.
US 20210280091 A1 Simpson; Ryan J. SYSTEM AND METHOD FOR BUILDING MACHINE LEARNING OR DEEP LEARNING DATA SETS FOR RECOGNIZING LABELS ON ITEMS - This application relates to a method and a system for building machine learning or deep learning data sets for automatically recognizing labels on items. The system may include an optical scanner configured to capture an item including one or more labels provided thereon, the item captured a plurality of times at different positions with respect to the optical scanner. The system may further include a robotic arm on which the item is disposed, the robotic arm configured to rotate the item horizontally and/or vertically such that the one or more labels of the item are captured by the optical scanner at different positions with respect to the optical scanner. The system may include a database configured to store the captured images.
Response to Amendments/Arguments
Applicant’s submitted remarks and arguments have been fully considered.
Applicant disagrees with the Office Action conclusions and asserts that the presented claims fully comply with the requirements of 35 U.S.C. § 101 regrading judicial exceptions. Further, Applicant is of the opinion that the prior art fails to teach Applicant’s invention.
Examiner respectfully disagrees in both regards.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 101.
Applicant submits:
a. The pending claims are not directed to an abstract idea.
b. The identified abstract idea is integrated into a practical application.
c. The pending claims amount to significantly more.
Furthermore, Applicant asserts that the Office has failed to meet its burden to identify the abstract idea and to establish that the identified abstract idea is not integrated into a practical application and that the pending claims do not amount to significantly more.
Examiner responds – The arguments have been considered in light of Applicants’ amendments to the claims. The arguments ARE NOT PERSUASIVE. Therefore, the rejection is maintained.
The pending claims, as a whole, are directed to an abstract idea not integrated into a practical application. This is because (1) they do not effect improvements to the functioning of a computer, or to any other technology or technical field (see MPEP 2106.05 (a)); (2) they do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or a medical condition (see the Vanda memo); (3) they do not apply the abstract idea with, or by use of, a particular machine (see MPEP 2106.05 (b)); (4) they do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05 (c)); (5) they do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the identified abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designated to monopolize the exception (see MPEP 2106.05 (e) and the Vanda memo).
In addition, the pending claims do not amount to significantly more than the abstract idea itself.
As such, the pending claims, when considered as a whole, are directed to an abstract idea not integrated into a practical application and not amounting to significantly more.
More specific:
Applicant submits “The "Mental Process" Rejection Violates the August 2025 Memo … The claims recite "deploying, by a hardware accelerator comprising a plurality of compute tiles, the plurality of machine learning models."”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
The eligibility analysis in the instant Office Action has determined that, based on the claim language and in light of the application specification, the claims are directed to evaluating and improving the visual quality of collated digital images. This is a process that a huma mind can and performs on a routine basis (e.g., .. the colors of this patch rug do not harmonize well … let’ make sone changes …). The hardware accelerator and the machine learning models are used only as a tool to improve the speed, operation performance and/or the quality of the end result.
Thus, the rejection is proper and has been maintained.
Applicant submits “The "Practical Application" Analysis Ignored Memo Directives and Evidence” … The Memo specifically instructs an examiner to "consult the specification to determine whether the disclosed invention improves technology." Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
It appears that Applicant refers to the provisions of MPEP 106.05(a). MPEP 2106.04(d)(1) discloses:
An important consideration to evaluate when determining whether the claim as a whole integrates a judicial exception into a practical application is whether the claimed invention improves the functioning of a computer or other technology .... In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art .... Second, if the specification sets forth an improvement in technology. the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. (Emphasis added)
That is, the claimed invention may integrate the judicial exception into a practical application by demonstrating that it improves the relevant existing technology although it may not be an improvement over well-understood, routine, conventional activity. (Emphasis added)
Thus, the rejection is proper and has been maintained.
Applicant submits “The Examiner clearly violated examination standards in maintaining the rejection. Applicant's Response explicitly cited paragraph [0013] of the specification, which details how the claimed hardware architecture provides a technical improvement by reducing instructions, increasing speed, and reducing latency. This failure to follow the Memo's procedure and consider the evidence of record is clear error.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
Applicant appears to argue that the claims are patent-eligible because they result in an improvement in the technology field. Examiner respectfully disagrees. It is not clear that the claims are directed to an improvement to an existing technology field. The claims appear directed to an improvement to the improvement of the evaluation of the image quality. The technological improvements identified by the courts in Diehr, Enfish, and Bascom are significantly different than programming a computer to better evaluation and improvement of collated images quality (see discussion below). As stated in MPEP § 2106.05(a) "[a]n indication that the claimed invention provides [a technological] improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art." The application disclosure fails to discuss or suggest an improvement to any underlying technical field executing the identified abstract idea. The original disclosure fails to discuss prior art image quality evaluation and improvement engines, as claimed by Applicant. In spite of disclosing at [0013] of the specification some perceived advantages which allegedly are brought about by the instant application, the original disclosure fails to discuss prior art image quality evaluation and improvement engines. The original disclosure therefore does not disclose, suggest, imply or allude to that the particular image quality evaluation and improvement engine structures being claimed are an improvement over prior art systems. The fact that the disclosure failed to identify a technical problem and the fact that the original disclosure fails to disclose, suggest, imply or allude to how or why the claimed arrangement of system elements enables an improvement, suggests that the claimed invention is not directed to this technical improvement. Instead, it appears Applicant has attempted to identify, after the fact, some unsubstantiated benefit of the claimed matter in an effort to exhibit that the claims are directed to a technical improvement. (see MPEP 2106.05(a); (i) specification requirements in regard to the technological improvements (should describe the improvement): McRO v Bandai – specification provides explanation, Affinity Labs – specification does not provide explanation; (ii) claim requirements in regard to the improvements (should recite the improvement): Enfish – claim reflects the improvement, Intellectual Ventures – claim does not reflect the improvement).
Thus, the rejection is proper and has been maintained.
It follows from the above that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. Therefore, the rejection under 35 U.S.C. § 101 is maintained.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 112(b).
The rejection is withdrawn as a result of the amendments.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 103.
Applicant submits that Tak does not disclose the claim limitation.
Examiner agrees. However, Zhang discloses:
programmatically combining different content assets among a set of content assets, including images, in different visual configuration resulting in multiple different digital components based on the set of content assets; {see at least [0003] Many remote sensing applications require images with both high spectral resolution and high spatial resolution. The multispectral images provide high spectral resolution, but low spatial resolution. On the other hand, the panchromatic images provide high spatial resolution, but low spectral resolution. Thus, methods for effectively fusing (combining) the multispectral and panchromatic images to produce fused high spatial resolution (also called pan-sharpened) color images are important. As a result, many image fusion methods have been developed, such as IHS (Intensity, Hue, Saturation), PCA (Principal Components Analysis), wavelet based fusion and SVR (Synthetic Variable Ratio). Among existing methods, the IHS and PCA fusion approaches have been the most popular ones.}
Therefore, Zang discloses the claim limitation.
Applicant submits “The Examiner Improperly Disregarded a Claim Limitation … The Examiner committed clear legal error by affording "no patentable weight" to the limitation "deploying, by a hardware accelerator comprising a plurality of compute tiles."”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
The instant Office Action does not invoke “no patentable weight.”
Second, although not invoked in any rejection, the term “hardware accelerator” is considered non-functional, descriptive data.
Thus, the rejection is proper and has been maintained.
Applicant submits “The claimed hardware accelerator is not merely descriptive, it is functionally integral to the claimed method. The "deploying"