DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
Claims 1-5, 8-15, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“An apparatus for evaluating the quality of digital human content representative of a computer-generated human, and included in a received source input including one or more of an image or video containing the digital human content, the apparatus comprising: a test method generation unit configured to receive, as information that queries a realism of the digital human content, test methods including identification information of questions and evaluation methods for the questions, and referring to a pre-stored question list and an evaluation method set, generate at least one of subjective test methods or objective test methods from the test methods; an evaluation result acquisition unit configured to obtain subjective evaluation results for the digital human content using the subjective test methods and obtain objective evaluation results for the digital human content using the objective test methods; and a quality evaluation unit configured to output a final evaluation result for the digital human content based on the subjective evaluation results and the objective evaluation results, wherein the evaluation result acquisition unit is configured to generate a questionnaire based on the subjective test methods and obtain the subjective evaluation results in response to providing the questionnaire to external testers, and wherein the evaluation result acquisition unit is configured to obtain the objective evaluation results by applying an external test tool set according to each objective test method to the source input.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional element”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
The steps of “a quality evaluation unit configured to output a final evaluation result for the digital human content based on the subjective evaluation results and the objective evaluation results” are treated as belonging to the mathematical calculations grouping and the steps of “wherein the evaluation result acquisition unit is configured to generate a questionnaire based on the subjective test methods and obtain the subjective evaluation results in response to providing the questionnaire to external testers, and wherein the evaluation result acquisition unit is configured to obtain the objective evaluation results by applying an external test tool set according to each objective test method to the source input” are treated as belonging to mental process grouping.
This mental step represents a process that, under its broadest reasonable
interpretation, covers performance of the limitation in the mind. That is, nothing in
the claim element precludes the step from practically being performed in the
mind. In the context of this claim, it encompasses the user manually making a
determination regarding obtaining the objective evaluation results by applying an external test tool set according to each objective test method to the source input.
Additionally, or alternatively, the abstract idea (all highlighted above limitations) is considered as falling into the groupings of organizing human activity –fundamental economic principles or practices (including hedging, insurance, mitigating risk). Such organizing human activity comprises, for example, activity of applying an external test tool set, i.e. mitigating risk of error/failure in obtaining the objective evaluation results.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that
integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: An apparatus for evaluating the quality of digital human content representative of a computer-generated human, and included in a received source input including one or more of an image or video containing the digital human content, the apparatus comprising: a test method generation unit configured to receive, as information that queries a realism of the digital human content, test methods including identification information of questions and evaluation methods for the questions, and referring to a pre-stored question list and an evaluation method set, generate at least one of subjective test methods or objective test methods from the test methods; an evaluation result acquisition unit configured to obtain subjective evaluation results for the digital human content using the subjective test methods and obtain objective evaluation results for the digital human content using the objective test methods.
Claim 11: A computer-implemented method for evaluating the quality of digital human content representative of a computer-generated human, and included in a received source input including one or more of an image or video containing the digital human content, the method comprising: receiving, as information that queries a realism of the digital human content, test methods including identification information of questions and evaluation methods for the questions; referring to a pre-stored question list and an evaluation method set, generating at least one of subjective test methods or objective test methods from the test methods; obtaining subjective evaluation results for the digital human content using the subjective test methods; obtaining objective evaluation results for the digital human content using the objective test methods; and outputting a final evaluation result for the digital human content based on the subjective evaluation results and the objective evaluation results.
The above additional element of an apparatus for evaluating the quality of digital human content representative of a computer-generated human, and included in a received source input including one or more of an image or video containing the digital human content, the apparatus comprising are generically recited, not meaningful, do not represent a particular machine and/or eligible transformation, they do not indicate a practical application and a test method generation unit configured to receive, as information that queries a realism of the digital human content, test methods including identification information of questions and evaluation methods for the questions, and referring to a pre-stored question list and an evaluation method set; an evaluation result acquisition unit configured to obtain subjective evaluation results for the digital human content using the subjective test methods and obtain objective evaluation results for the digital human content using the objective test methods corresponds to mere data gathering that is recited in generality and is not meaningful- represents insignificant extra-solution activity, and generate at least one of subjective test methods or objective test methods from the test methods corresponds to an insignificant extra solution activity of outputting results.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record including references (Kim and Zhang).
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-10 and 12-20 provide additional features/steps which are either part of an expanded abstract idea of the independent claims (additionally comprising mathematical/mental/organizing human activity process steps (Claims 2-10 and 12-20) or adding additional elements/steps that are not meaningful as they are recited in generality and/or not qualified as particular machine/ and/or eligible transformation and, therefore, do not reflect a practical application as well as not qualified for “significantly more” based on prior art of record.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US 20050267726), hereinafter referred to as ‘Kim' and in further view of Van Zon et al. (US 20020090134), hereinafter referred to as ‘Van Zon' and Zhang et al. (US20220292654), hereinafter referred to as ‘Zhang’.
Regarding Claim 1, Kim discloses an apparatus for evaluating the quality of digital human content representative of a computer-generated human, and included in a received source input including one or more of an image or video containing the digital human content, the apparatus comprising (It is, therefore, an object of the present invention to provide an apparatus and a method for predicting reality of an image through sequential operations [0006]): a test method generation unit configured to receive test methods including identification information of questions and evaluation methods for the questions (First, in the case of carrying out the psychophysical observer test by asking questions after displaying two images on the display device and receiving the answers, the details of the questions are as follows [0038]; [0039]-[0042]): a test method generation unit configured to receive, as information that queries a realism of the digital human content (The observer testing block 140 includes: an image display unit 141 for sequentially displaying the converted first test images provided from the image converting block 130 on a display device; and an observer input unit 142 to which answers for image reality related questions about the displayed images are inputted by the observers [0034]), test methods including identification information of questions and evaluation methods for the questions (The prediction model verifier 110 applies the image reality prediction model generated from the prediction model generator 100 to a second test image inputted from outside to predict the image reality and, compares the prediction result with the observer test result to verify accuracy of the image reality prediction model. The reality prediction model applier 120 applies the image reality prediction model verified by the prediction model verifier 110 to a produced image actually targeted for a reality evaluation and then outputs the reality prediction result [0020]), and referring to a pre-stored question list and an evaluation method set (A. Are the two displayed images the same in overall? [0039]), generate at least one of subjective test methods or objective test methods from the test methods (That is, the standardized systematic workflow can provide the subjectively evaluated overall image quality in an objective numerical value. Also, since the image reality prediction model is generated based on a statistically similar group of observers, it is possible to maintain the test result with high reliability [0061]); an evaluation result acquisition unit configured to obtain subjective evaluation results for the digital human content (That is, the standardized systematic workflow can provide the subjectively evaluated overall image quality in an objective numerical value. Also, since the image reality prediction model is generated based on a statistically similar group of observers, it is possible to maintain the test result with high reliability [0061]); and a quality evaluation unit configured to output a final evaluation result for the digital human content (Next, the verified image reality prediction model is applied to a produced image actually targeted for the image reality evaluation to predict the reality of the produced image. Afterwards, the prediction result is outputted thereafter [0054]) wherein the evaluation result acquisition unit is configured to generate a questionnaire (As for the sequential operations for the psychophysical observer test, the observer testing block 140 sequentially displays the plurality of converted first test images through the image display unit 141, which receives the plurality of converted first test images from the image converting block 130 and then, displays the converted first test images sequentially on the display device and, carries out the psychophysical observer test as receiving answers for a series of image reality related questions about the displayed first test images from the observers through the observer input unit 142 and outputs the test result data thereafter [0035]).
However, Kim does not explicitly disclose an evaluation result acquisition unit configured to obtain subjective evaluation results for the digital human content using the subjective test methods and obtain objective evaluation results for the digital human content using the objective test methods; and a quality evaluation unit configured to output a final evaluation result for the digital human content based on the subjective evaluation results and the objective evaluation results, wherein the evaluation result acquisition unit is configured to generate a questionnaire based on the subjective test methods and obtain the subjective evaluation results in response to providing the questionnaire to external testers, and wherein the evaluation result acquisition unit is configured to obtain the objective evaluation results by applying an external test tool set according to each objective test method to the source input.
Nevertheless, Van Zon discloses generate at least one of subjective test methods or objective test methods from the test methods (The values of X(i, j) are the values of a set of an objective data values for a video image. The values of Y(i, j) are the values of a set of n subjective data values for the same video image. That is, the number of X data points (n) is the same number of Y data points (n) [0046]; It is an additional object of the present invention to provide a scalable objective metric from correlation factor derived using a neural network algorithm that employs both objective quality scores and subjective quality scores [0020]); using the subjective test methods and obtain objective evaluation results for the digital human content using the objective test methods (It is an additional object of the present invention to provide a scalable objective metric from correlation factor derived using a neural network algorithm that employs both objective quality scores and subjective quality scores [0020]); and a quality evaluation unit configured to output a final evaluation result for the digital human content based on the subjective evaluation results and the objective evaluation results (It is an additional object of the present invention to provide a scalable objective metric from correlation factor derived using a neural network algorithm that employs both objective quality scores and subjective quality scores [0020]; Controller 150 operates in conjunction with an operating system (not shown) located within memory 160 to process data, to store data, to retrieve data and to output data. Controller 150 calculates scalable objective metric "F" by executing computer instructions stored in memory 160 [0034]), wherein the evaluation result acquisition unit is configured to generate a questionnaire based on the subjective test methods and obtain the subjective evaluation results in response to providing the questionnaire to external testers (It is an additional object of the present invention to provide a scalable objective metric from correlation factor derived using a neural network algorithm that employs both objective quality scores and subjective quality scores [0020]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim with the teachings of Van Zon to include both objective and subjective data while improving the accuracy of the image reality prediction model.
However, the combination does not explicitly disclose the evaluation result acquisition unit is configured to obtain the objective evaluation results by applying an external test tool set according to each objective test method to the source input.
Nevertheless, Zhang discloses applying an external test tool set ( For example, FIG. 9 illustrates a table 902 of performance metrics associated with digital image compositing for different systems in accordance with one or more embodiments. Specifically, the table 902 illustrates columns for metrics such as peak signal-to-noise ratio (“PSNR”), mean squared error (“MSE”), structural similarity index measure (“SSIM”), i.e., external test tool, and learned perceptual patch similarity (“LPIPS”) [0113]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim and Van Zon with the teachings of Zhang to measure the similarity between two images and evaluate changes in structural information and improve the accuracy of the model.
Regarding Claim 2, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 1.
Kim discloses the test method generation unit is configured to collect question items corresponding to the identification information of the questions from the question list (The observer testing block 140 includes: an image display unit 141 for sequentially displaying the converted first test images provided from the image converting block 130 on a display device; and an observer input unit 142 to which answers for image reality related questions about the displayed images are inputted by the observers [0034]; [0039]-[0042]) and generate at least one of the subjective test methods or the objective test methods by combining the question items with the subjective evaluation methods or the objective evaluation methods (The above standardized systematic workflow provides an effect that conventional image quality evaluation methods providing a mathematical analysis result based on a physical difference, for instance, a signal-to-noise ratio and an analysis on a difference between pixels of a digital image, cannot provide. That is, the standardized systematic workflow can provide the subjectively evaluated overall image quality in an objective numerical value [0061]).
However, Kim does not explicitly disclose identify subjective evaluation methods and objective evaluation methods from the evaluation methods using the evaluation method set.
Nevertheless, Van Zon discloses identify subjective evaluation methods and objective evaluation methods from the evaluation methods using the evaluation method set (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim and Van Zon with the teachings of Zhang to include both objective and subjective data while improving the accuracy of the image reality prediction model.
Regarding Claim 3, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 1.
Kim discloses the evaluation method set comprises identification information of subjective evaluation methods (as discussed above).
However, Kim does not explicitly disclose the evaluation method set comprises identification information of subjective evaluation methods and identification information of objective evaluation methods.
Nevertheless, Van Hon discloses subjective evaluation methods and objective evaluation methods (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim and Van Zon with the teachings of Zhang to include both objective and subjective data while improving the accuracy of the image reality prediction model.
Regarding Claim 4, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 1.
Kim discloses the test method generation unit is configured to select a part of the test methods based on the characteristics of the digital human content in the source input and generate at least one of the subjective test methods or the objective test methods from the selected test methods (as discussed above).
Regarding Claim 5, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 4.
Kim discloses the test method generation unit is configured to select the part of the test methods (as discussed above).
However, Kim discloses the test method generation unit is configured to select the part of the test methods based further on a previous final evaluation result for the digital human content.
Nevertheless, Van Zon discloses final evaluation result for the digital human content (Because the ultimate goal is to present the viewer with the most appealing picture, a final judge of the value of the objective measures of video quality is the degree of correlation that the objective measures have with the subjective results [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim and Van Zon with the teachings of Zhang to correlate the results objectively with results subjectively obtained to minimize error and improve accuracy.
Regarding Claim 8, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 1.
Kim discloses the quality evaluation unit is configured to calculate subjective evaluation scores for the subjective test methods (as discussed above) and objective evaluation scores for the objective test methods based on the subjective evaluation results and the objective evaluation results, normalize the subjective evaluation scores and the objective evaluation scores, and output the final evaluation result including the normalized subjective evaluation scores and the normalized objective evaluation scores.
However, Kim does not explicitly disclose the quality evaluation unit is configured to calculate subjective evaluation scores for the subjective test methods and objective evaluation scores for the objective test methods based on the subjective evaluation results and the objective evaluation results, normalize the subjective evaluation scores and the objective evaluation scores, and output the final evaluation result including the normalized subjective evaluation scores and the normalized objective evaluation scores.
Nevertheless, Van Zon discloses objective evaluation scores for the objective test methods based on the subjective evaluation results and the objective evaluation results (as discussed above), normalize the subjective evaluation scores and the objective evaluation scores (Results solely based upon on human perception and subjective opinion are usually subjected to subsequent statistical analysis to remove ambiguities that result from the non-deterministic nature of subjective results. Linear and non-linear heuristic statistical models have been proposed to normalize these types of subjective results and obtain certain figures of merit that represent the goodness (or the degradation) of video quality [0003]), and output the final evaluation result including the normalized subjective evaluation scores and the normalized objective evaluation scores (Results solely based upon on human perception and subjective opinion are usually subjected to subsequent statistical analysis to remove ambiguities that result from the non-deterministic nature of subjective results. Linear and non-linear heuristic statistical models have been proposed to normalize these types of subjective results and obtain certain figures of merit that represent the goodness (or the degradation) of video quality [0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim and Van Zon with the teachings of Zhang to include both objective and subjective data while improving the accuracy of the image reality prediction model.
Regarding Claim 9, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 8.
Kim discloses the quality evaluation unit is configured to output (as discussed above).
However, Kim does not explicitly disclose the quality evaluation unit is configured to output the final evaluation result including an average of weighted scores calculated by applying predetermined weights for each of the subjective test methods and the objective test methods to the normalized subjective evaluation scores and the normalized objective evaluation scores.
Nevertheless, Van Zon discloses the quality evaluation unit is configured to output the final evaluation result including an average of weighted scores calculated by applying predetermined weights for each of the subjective test methods (The scalable objective metric of the present invention avoids the shortcomings of any single objective metric. This is because weighting unit 190 will assign a low value to w(i) for any objective metric that performs poorly in the presence of a certain set of artifacts. The scalable objective metric of the present invention achieves the highest correlation with the results of subjective testing when compared any single objective metric [0052]) and the objective test methods to the normalized subjective evaluation scores and the normalized objective evaluation scores (The scalable objective metric of the present invention avoids the shortcomings of any single objective metric. This is because weighting unit 190 will assign a low value to w(i) for any objective metric that performs poorly in the presence of a certain set of artifacts. The scalable objective metric of the present invention achieves the highest correlation with the results of subjective testing when compared any single objective metric [0052]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim and Van Zon with the teachings of Zhang to achieve the highest correlation with the results of subjective testing when compared any single objective metric and improve accuracy of the model.
Regarding Claim 10, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 1.
Kim discloses identification information of the questions and identification information of the evaluation methods for the questions (as discussed above).
However, Kim does not explicitly disclose the final evaluation result comprises subjective evaluation scores and objective evaluation scores calculated based on the subjective evaluation results and the objective evaluation results.
Nevertheless, Van Zon discloses the final evaluation result comprises subjective evaluation scores and objective evaluation scores calculated based on the subjective evaluation results and the objective evaluation results (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim and Van Zon with the teachings of Zhang to achieve the highest correlation with the results of subjective testing when compared any single objective metric and improve accuracy of the model.
Claims 11-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim and Van Zon.
Regarding Claim 11, Kim discloses a computer-implemented method for evaluating the quality of digital human content included in a source input, the method comprising (It is, therefore, an object of the present invention to provide an apparatus and a method for predicting reality of an image through sequential operations [0006]): receiving test methods including identification information of questions and evaluation methods for the questions (First, in the case of carrying out the psychophysical observer test by asking questions after displaying two images on the display device and receiving the answers, the details of the questions are as follows [0038]; [0039]-[0042]), referring to a pre-stored question list and an evaluation method set (A. Are the two displayed images the same in overall? [0039]), generating at least one of subjective test methods or objective test methods from the test methods (That is, the standardized systematic workflow can provide the subjectively evaluated overall image quality in an objective numerical value. Also, since the image reality prediction model is generated based on a statistically similar group of observers, it is possible to maintain the test result with high reliability [0061]); obtaining subjective evaluation results for the digital human content using the subjective test methods; obtaining objective evaluation results for the digital human content using the objective test methods (That is, the standardized systematic workflow can provide the subjectively evaluated overall image quality in an objective numerical value. Also, since the image reality prediction model is generated based on a statistically similar group of observers, it is possible to maintain the test result with high reliability [0061]); and outputting a final evaluation result for the digital human content based on the subjective evaluation results and the objective evaluation results (Next, the verified image reality prediction model is applied to a produced image actually targeted for the image reality evaluation to predict the reality of the produced image. Afterwards, the prediction result is outputted thereafter [0054]).
However, Kim does not explicitly disclose obtaining objective evaluation results for the digital human content using the objective test methods; and outputting a final evaluation result for the digital human content based on the subjective evaluation results and the objective evaluation results.
Nevertheless, Van Zon discloses obtaining objective evaluation results for the digital human content using the objective test methods (It is an additional object of the present invention to provide a scalable objective metric from correlation factor derived using a neural network algorithm that employs both objective quality scores and subjective quality scores [0020]); outputting a final evaluation result for the digital human content based on the subjective evaluation results and the objective evaluation results (It is an additional object of the present invention to provide a scalable objective metric from correlation factor derived using a neural network algorithm that employs both objective quality scores and subjective quality scores [0020]; Controller 150 operates in conjunction with an operating system (not shown) located within memory 160 to process data, to store data, to retrieve data and to output data. Controller 150 calculates scalable objective metric "F" by executing computer instructions stored in memory 160 [0034]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim with the teachings of Van Zon to include both objective and subjective data while improving the accuracy of the image reality prediction model.
Regarding Claim 12, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 11.
Kim discloses the generating comprises: collecting question items corresponding to the identification information of the questions from the question list (The observer testing block 140 includes: an image display unit 141 for sequentially displaying the converted first test images provided from the image converting block 130 on a display device; and an observer input unit 142 to which answers for image reality related questions about the displayed images are inputted by the observers [0034]; [0039]-[0042]) generating at least one of the subjective test methods or the objective test methods by combining the question items with the subjective evaluation methods or the objective evaluation methods (The above standardized systematic workflow provides an effect that conventional image quality evaluation methods providing a mathematical analysis result based on a physical difference, for instance, a signal-to-noise ratio and an analysis on a difference between pixels of a digital image, cannot provide. That is, the standardized systematic workflow can provide the subjectively evaluated overall image quality in an objective numerical value [0061]).
However, Kim does not explicitly disclose identifying subjective evaluation methods and objective evaluation methods from the evaluation methods using the evaluation method set.
Nevertheless, Van Zon discloses identifying subjective evaluation methods and objective evaluation methods from the evaluation methods using the evaluation method set (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim with the teachings of Van Zon to include both objective and subjective data while improving the accuracy of the image reality prediction model.
Regarding Claim 13, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 11.
Kim discloses the evaluation method set comprises identification information of subjective evaluation methods (as discussed above).
However, Kim does not explicitly disclose the evaluation method set comprises identification information of subjective evaluation methods and identification information of objective evaluation methods.
Nevertheless, Van Hon discloses subjective evaluation methods and objective evaluation methods (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim with the teachings of Van Zon to include both objective and subjective data while improving the accuracy of the image reality prediction model.
Regarding Claim 14, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 11.
Kim discloses the generating comprises: selecting a part of the test methods based on the characteristics of the digital human content in the source input; and generating at least one of the subjective test methods or the objective test methods from the selected test methods (as discussed above).
Regarding Claim 15, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 14.
Kim discloses selecting comprises selecting the part of the test methods (as discussed above).
However, Kim discloses selecting comprises selecting the part of the test methods based further on a previous final evaluation result for the digital human content.
Nevertheless, Van Zon discloses final evaluation result for the digital human content (Because the ultimate goal is to present the viewer with the most appealing picture, a final judge of the value of the objective measures of video quality is the degree of correlation that the objective measures have with the subjective results [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim with the teachings of Van Zon to correlate the results objectively with results subjectively obtained to minimize error and improve accuracy.
Regarding Claim 18, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 11.
Kim discloses outputting comprises: calculating subjective evaluation scores for the subjective test methods (as discussed above).
However, Kim does not explicitly disclose the outputting comprises: calculating subjective evaluation scores for the subjective test methods and objective evaluation scores for the objective test methods based on the subjective evaluation results and the objective evaluation results; normalizing the subjective evaluation scores and the objective evaluation scores; and outputting the final evaluation result including the normalized subjective evaluation scores and the normalized objective evaluation scores.
Nevertheless, Van Zon discloses the outputting comprises: calculating subjective evaluation scores for the subjective test methods (as discussed above), normalizing the subjective evaluation scores and the objective evaluation scores; (Results solely based upon on human perception and subjective opinion are usually subjected to subsequent statistical analysis to remove ambiguities that result from the non-deterministic nature of subjective results. Linear and non-linear heuristic statistical models have been proposed to normalize these types of subjective results and obtain certain figures of merit that represent the goodness (or the degradation) of video quality [0003]), and outputting the final evaluation result including the normalized subjective evaluation scores and the normalized objective evaluation scores (Results solely based upon on human perception and subjective opinion are usually subjected to subsequent statistical analysis to remove ambiguities that result from the non-deterministic nature of subjective results. Linear and non-linear heuristic statistical models have been proposed to normalize these types of subjective results and obtain certain figures of merit that represent the goodness (or the degradation) of video quality [0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim with the teachings of Van Zon to include both objective and subjective data while improving the accuracy of the image reality prediction model.
Regarding Claim 19, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 18.
Kim discloses the outputting the final evaluation result comprises outputting the final evaluation result (as discussed above).
However, Kim does not explicitly disclose outputting the final evaluation result comprises outputting the final evaluation result including an average of weighted scores calculated by applying predetermined weights for each of the subjective test methods and the objective test methods to the normalized subjective evaluation scores and the normalized objective evaluation scores.
Nevertheless, Van Zon discloses outputting the final evaluation result comprises outputting the final evaluation result including an average of weighted scores calculated by applying predetermined weights for each of the subjective test methods (The scalable objective metric of the present invention avoids the shortcomings of any single objective metric. This is because weighting unit 190 will assign a low value to w(i) for any objective metric that performs poorly in the presence of a certain set of artifacts. The scalable objective metric of the present invention achieves the highest correlation with the results of subjective testing when compared any single objective metric [0052]) the objective test methods to the normalized subjective evaluation scores and the normalized objective evaluation scores (The scalable objective metric of the present invention avoids the shortcomings of any single objective metric. This is because weighting unit 190 will assign a low value to w(i) for any objective metric that performs poorly in the presence of a certain set of artifacts. The scalable objective metric of the present invention achieves the highest correlation with the results of subjective testing when compared any single objective metric [0052]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim with the teachings of Van Zon to achieve the highest correlation with the results of subjective testing when compared any single objective metric and improve accuracy of the model.
Regarding Claim 20, Kim, Van Zon, and Zhang disclose the claimed invention discussed in claim 11.
Kim discloses identification information of the questions and identification information of the evaluation methods for the questions (as discussed above).
However, Kim does not explicitly disclose the final evaluation result comprises subjective evaluation scores and objective evaluation scores calculated based on the subjective evaluation results and the objective evaluation results.
Nevertheless, Van Zon discloses the final evaluation result comprises subjective evaluation scores and objective evaluation scores calculated based on the subjective evaluation results and the objective evaluation results (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kim with the teachings of Van Zon to achieve the highest correlation with the results of subjective testing when compared any single objective metric and improve accuracy of the model.
Response to Arguments
35 USC § 112
Applicant’s arguments, filed 01/29/2026, with respect to claims 1-5, 8-15, and 18-20 have been fully considered and are persuasive. The rejection of claims 1-5, 8-15, and 18-20 have been withdrawn.
35 USC § 101
Applicant's arguments filed 01/29/2026 have been fully considered but they are not persuasive.
The Applicant argues (pg. 15): “Applicants respectfully submit that the present claims are not directed to "mathematical calculations," or mathematical concepts, as indicated in the above-noted Guidance. That is, the above-noted claimed features of independent claim 1 (and independent claim 10) are not, and/or would/could not be corresponding to mathematical calculations, as indicated in the above-noted Guidance. Therefore, the present claims do not recite an abstract idea under the 2019 Revised Patent Subject Matter Eligibility Guidance. Additionally, the Office Action has improperly asserted that the claims are directed to an abstract idea. No reasoned rationale has been provided…In the present case, the Office has failed to provide any rationale evidencing that the claimed subject matter is similar to what the courts have identified as an abstract idea, and has failed to provide any citation of any court-identified cases with respect to the above- noted claimed features as a whole”
The Examiner respectfully disagrees and submits “…claimed features of independent claim 1 (and independent claim 10)…” the claimed features recited in the NFOA as belonging to mathematical grouping were indicated as such because [0053] of the specification states that the quality evaluation unit performs calculations in order to determine the evaluation scores. Additionally, according to MPEP 2106.04(a)(2), It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas).
The Applicant argues (pg.16-17): “The claims are directed to a specific, concrete, technological solution that provides a measurable improvement to the technical field of digital-human quality assessment… Specifically, the claims improve an existing technology as articulated at least by the following paragraphs of the originally filed specification: [0004] With the spread of metaverse services in recent years, the demand for digital human content is continuously increasing due to high-quality 3D models in game services that were previously provided, remote medical care due to the COVID- 19 situation, and human body simulations for pharmaceutical research… The instant application states that there is currently no objective, standardized, repeatable manner to measure the quality or realism of digital humans, even though the metaverse, medical simulation, and gaming industries desperately need one. The instant application and claims improves the existing technology by reciting a particular computerized architecture…The claimed operations are similar to the eligible claims in McRO, Inc. v. Bandai Namco Games America, Inc., No. 15-1080, that was heard on September 13, 2016. The original specification repeatedly states that prior to this application there was no objective, standardized, repeatable way to measure the quality/realism of digital humans, even though the metaverse, medical simulation, and gaming industries desperately needed one. This is exactly the type of technological improvement recognized in McRO, Inc., Enfish, and USPTO Example 39/47 “
The Examiner respectfully disagrees and submits that improvements in the technology are realized through meaningful additional elements. The additional elements outlined in the NFOA were identified as being insignificant extra-solution activities, i.e., data gathering. Additionally, according to MPEP 2106.05(f), “In contrast, other cases have found that additional elements are more than "apply it" or are not "mere instructions" when the claim recites a technological solution to a technological problem. In DDR Holdings, the court found that the additional elements did amount to more than merely instructing that the abstract idea should be applied on the Internet. DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1259, 113 USPQ2d 1097, 1107 (Fed. Cir. 2014). The claims at issue specified how interactions with the Internet were manipulated to yield a desired result—a result that overrode the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.”
The Applicant argues (pg. 18): “The claims of the instant application integrates any Office-perceived abstract idea into a practical application via specific technical outputs (three-dimensional (3D) avatars and real-time synchronization), and go beyond generic automation. The claims integrate any alleged abstract idea into a practical, technically specific method for measuring the quality or realism of digital humans. The feedback process which uses previous evaluation results or digital-human characteristics to select only relevant test methods, also provides the required technological integration and inventive concept, and is machine-learning-like adaptation, thus further evidencing a technological improvement.”
The Examiner respectfully disagrees that “The claims of the instant application integrates any Office-perceived abstract idea into a practical application via specific technical outputs (three-dimensional (3D) avatars and real-time synchronization), and go beyond generic automation. The claims integrate any alleged abstract idea into a practical, technically specific method for measuring the quality or realism of digital humans. The feedback process which uses previous evaluation results or digital-human characteristics to select only relevant test methods, also provides the required technological integration and inventive concept, and is machine-learning-like adaptation, thus further evidencing a technological improvement” the current claims limitations were either identified as belonging to an abstract idea, i.e., mathematical process grouping or additional elements, i.e., data gathering. According to MPEP 2106.05(c), “"[T]ransformation and reduction of an article ‘to a different state or thing’ is the clue to patentability of a process claim that does not include particular machines." Bilski v. Kappos, 561 U.S. 593, 658, 95 USPQ2d 1001, 1007 (2010) (quoting Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972)).”
The Applicant argues (pg. 18): “Accordingly, it is respectfully submitted that it is impossible to readily conclude that the numerous elements, individually, as a whole, in an ordered combination, or in inventive concept implementation do not amount to significantly more than an abstract idea. Specifically, the claims amount to significantly more than an abstract idea when considering the claims as a whole…Moreover, Applicants respectfully submit that the ordered combination of parsing structured test-case inputs, dynamic generation of hybrid subjective/objective test methods, normalization and weighted combination into a final quality score is not well-understood, routine, or conventional. The specification provides evidence that this particular combination solves a problem that the industry had not solved before”
The Examiner respectfully disagrees and submits that “Accordingly, it is respectfully submitted that it is impossible to readily conclude that the numerous elements, individually, as a whole, in an ordered combination, or in inventive concept implementation do not amount to significantly more than an abstract idea...The specification provides evidence that this particular combination solves a problem that the industry had not solved before” the claims lack meaningful additional and/or significantly more elements that would indicate an improvement and the applicant is claiming improvements in technology are being executed by the abstract ideas and they must be executed by meaningful additional elements.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARAH ZAAB whose telephone number is (571)272-4973. The examiner can normally be reached Monday - Friday 7:00 am - 4:30 pm.
/SHARAH ZAAB/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857