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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/23/2026 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 8, 11, and 18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The independent claims recite “…a quality evaluation unit configured to compute a scalar result value and a vector result value…” while the specification [0059] recites “…a scalar result or a vector…” The claim limitation does not coincide with the specification as it relates to the use of the words “or” or “not”.
Claim Rejections - 35 USC § 101
35 U.S.C. 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 1, 8, 11, and 18 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 a quality of digital human content included in a received source input, wherein the digital human content represents computer-generated human, and the source input includes one or more of an image or a video containing the digital human content, the apparatus comprising: a test method selection unit, comprising: a parser configured to receive and analyze a test case input including identification information of a test case, a number of test methods, and, for each test method instance, a weight, a question list identifier indicating a question list used in the test case, and an evaluation method: and a selector configured to identify, from a pre-stored question list, a question item corresponding to the question list identifier, identify an evaluation method by referring to a pre- stored evaluation method set, and select a test method by combining the identified question item with the identified evaluation method; an evaluation result acquisition unit configured to, if the test method is a subjective test method, generate a questionnaire based on the test method, provide the generated questionnaire to external testers, and obtain an evaluation score for the generated questionnaire and a quality evaluation unit configured to compute a scalar result value and a vector result value by applying the weight included in the test case input to the evaluation score, output a final evaluation result for the digital human content including the scalar result value and the vector result value, and feedback the output final evaluation result to the test method selection unit to wherein the test method represents information that queries a realism of the source input, wherein the final evaluation result includes a test case output, wherein the test case output includes test information indicating a number of the testers, the scalar result value, the vector result value, and the test case input, wherein the test case input is updated based on the final evaluation result and a characteristic of the digital human content, and wherein the selector of the test method selection unit is further configured to select the test method based on the final evaluation result fed back from the quality evaluation unit”.
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 “an evaluation result acquisition unit configured to, if the test method is a subjective test method, generate a questionnaire based on the test method, provide the generated questionnaire to external testers, and obtain an evaluation score for the generated questionnaire and a quality evaluation unit configured to compute a scalar result value and a vector result value by applying the weight included in the test case input to the evaluation score” are treated as belonging to the mathematical calculations grouping and the steps of “a parser configured to receive and analyze a test case input including identification information of a test case, a number of test methods, and, for each test method instance, a weight, a question list identifier indicating a question list used in the test case, and an evaluation method: and a selector configured to identify, from a pre-stored question list, a question item corresponding to the question list identifier, identify an evaluation method by referring to a pre- stored evaluation method set, and select a test method by combining the identified question item with the identified evaluation method and wherein the selector of the test method selection unit is further configured to select the test method based on the final evaluation result fed back from the quality evaluation unit” 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 analyze test input, identify and select a test method and make 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 a quality of digital human content included in a received source input, wherein the digital human content represents computer-generated human, and the source input includes one or more of an image or a video containing the digital human content, the apparatus comprising: a test method selection unit, comprising: output a final evaluation result for the digital human content including the scalar result value and the vector result value, and feedback the output final evaluation result to the test method selection unit to, wherein the test method represents information that queries a realism of the source input, wherein the final evaluation result includes a test case output, wherein the test case output includes test information indicating a number of the testers, the scalar result value, the vector result value, and the test case input, wherein the test case input is updated based on the final evaluation result and a characteristic of the digital human content
Claim 11: A computer-implemented method for evaluating the a quality of digital human content included in a received source input, wherein the digital human content represents a computer-generated human, and included the source input includes one or more of an image or a video containing the digital human content, the method comprising, a test method selection unit, comprising, outputting a final evaluation result for the digital human content including the scalar result value and the vector result value, and feeding back the output final evaluation result to the test method selection unit, wherein the test method represents information that queries a realism of the source input, wherein the final evaluation result includes a test case output, wherein the test case output includes test information indicating a number of the testers, the scalar result value, the vector result value, and the test case input, wherein the test case input is updated based on the final evaluation result and a characteristic of the digital human content, and wherein the selecting selects the test method further based on the final evaluation result fed back from the quality evaluation unit
The above additional element of an apparatus for evaluating a quality of digital human content included in a received source input, wherein the digital human content represents computer-generated human, and the source input includes one or more of an image or a video containing the digital human content, the apparatus comprising: a test method selection unit, comprising are generically recited, not meaningful, do not represent a particular machine and/or eligible transformation, they do not indicate a practical application, wherein the test method represents information that queries a realism of the source input, wherein the final evaluation result includes a test case output, wherein the test case output includes test information indicating a number of the testers, the scalar result value, the vector result value, and the test case input, wherein the test case input is updated based on the final evaluation result and a characteristic of the digital human content are generically recited and represent mere data gathering steps necessary to execute the abstract idea, and outputting a final evaluation result for the digital human content including the scalar result value and the vector result value, and feedback the output final evaluation result to the test method selection unit represent a post solution activity of outputting results and are necessary to execute the abstract idea.
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 8 and18 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 8 and 18) 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, 8, 11, and 18 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 Kimmerling et al. (US 20130325627), hereinafter referred to as ‘Kimmerling'.
Regarding Claim 1, Kim discloses an apparatus for evaluating a quality of digital human content included in a received source input, wherein the digital human content represents computer-generated human, and the source input includes one or more of an image or a video containing the digital human content, the apparatus comprising: a test method selection unit, comprising: a parser configured to receive and analyze a test case input including identification information of a test case, a number of test methods, and, for each test method instance, a weight, a question list identifier indicating a question list used in the test case (In more detail of the sequential operations for the image reality prediction, a plurality of first test images are converted by using predetermined parameters affecting the image reality. Then, the converted first test images are displayed sequentially on a monitor used for the psychophysical observer test, which is subsequently applied to observers who are statistically classified into a similar group, and the test data are collected [0007]; [0038]-[0043]; [0047]), and an evaluation method: and a selector configured to identify, from a pre-stored question list, a question item corresponding to the question list identifier, identify an evaluation method by referring to a pre- stored evaluation method set, and select a test method by combining the identified question item with the identified evaluation method (At this time, the observer testing block 140 carries out the psychophysical observer test, i.e., evaluation method, by asking questions, i.e., pre-stored question list, after displaying one image on the display device and receiving answers, or by asking questions after displaying two images on the display device and receiving answers, i.e., [0036]); an evaluation result acquisition unit configured to, if the test method is a subjective test method, generate a questionnaire based on the test method, provide the generated questionnaire to external testers ([0038]-[0043]), and obtain an evaluation score for the generated questionnaire (a Z-score generating unit 152 for generating Z-score data, which are statistical analysis results, through using the sorted data inputted from the data sorting unit 151 [0049]), output a final evaluation result (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 feedback the output final evaluation result to the test method selection unit (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]) to wherein the test method represents information that queries a realism of the source input (For instance, the questions related to the image reality include the following details for each of the above described testing methods [0037]), wherein the final evaluation result includes a test case output (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 a test method selection unit, comprising: a parser configured to receive and analyze a test case input including identification information of a test case, a number of test methods, and, for each test method instance, a weight, a question list identifier indicating a question list used in the test case, and obtain an evaluation score for the generated questionnaire and a quality evaluation unit configured to compute a scalar result value and a vector result value by applying the weight included in the test case input to the evaluation score, output a final evaluation result for the digital human content including the scalar result value and the vector result value, wherein the test case output includes test information indicating a number of the testers, the scalar result value, the vector result value, and the test case input, wherein the test case input is updated based on the final evaluation result and a characteristic of the digital human content, and wherein the selector of the test method selection unit is further configured to select the test method based on the final evaluation result fed back from the quality evaluation unit.
Nevertheless, Kimmerling discloses a test method selection unit, comprising: a parser configured to receive and analyze a test case input including identification information of a test case, a number of test methods, and, for each test method instance, a weight, a question list identifier indicating a question list used in the test case (Biasing may be accomplished by weighing the group preference vector to reflect one person's misery being more important than another person misery. The item scores are calculated for each item based on the score from each individual (A and B). An even weight is determined by the formula W=100-min(100-scoreA, 100-scoreB) or, as described above, by the average of the scores [0090]), and obtain an evaluation score for the generated questionnaire and a quality evaluation unit configured to compute a scalar result value and a vector result value by applying the weight included in the test case input to the evaluation score (System 130 may include an API 132, an application program 134, and a database 140. Database 140 may include item preference data 158 and item scores 160 computed based on a user preference vector 170, which is computed by processing responses 120. Database 140 may also include a feature matrix 150 including item preference vectors 152 obtained from item preference data 158 [0042]), output a final evaluation result including the scalar result value and the vector result value (System 130 may include an API 132, an application program 134, and a database 140. Database 140 may include item preference data 158 and item scores 160 computed based on a user preference vector 170, which is computed by processing responses 120. Database 140 may also include a feature matrix 150 including item preference vectors 152 obtained from item preference data 158 [0042]; A way to bias in favor of A is determined by the formula Wa=100-min(100-scoreA, (100-scoreB)*Scalar), where Scalar is less than 1 (for instance 0.5). Alternatively, a weight to bias in favor of A is determined by the formula Wa'=(scoreA+ScoreB*Scalar)/(1+Scalar), where Scalar is less than 1 (for instance 0.5) [0090]), wherein the test case output includes test information indicating a number of the testers, the scalar result value, the vector result value(System 130 may include an API 132, an application program 134, and a database 140. Database 140 may include item preference data 158 and item scores 160 computed based on a user preference vector 170, which is computed by processing responses 120. Database 140 may also include a feature matrix 150 including item preference vectors 152 obtained from item preference data 158 [0042]; A way to bias in favor of A is determined by the formula Wa=100-min(100-scoreA, (100-scoreB)*Scalar), where Scalar is less than 1 (for instance 0.5). Alternatively, a weight to bias in favor of A is determined by the formula Wa'=(scoreA+ScoreB*Scalar)/(1+Scalar), where Scalar is less than 1 (for instance 0.5) [0090]), wherein the test case input is updated based on the final evaluation result and a characteristic of the digital human content (In another variation, the item preference data is updated by folding-in additional items so that the system and method for eliciting information stays current. The item preference data is updated over time by folding-in additional items…In the case of movies, additional items may include movies not included in the item preference data, such as movies released after the last update of the item preference data [0048]), and wherein the selector of the test method selection unit is further configured to select the test method based on the final evaluation result fed back from the quality evaluation unit (In another variation, the item preference data is updated by folding-in additional items so that the system and method for eliciting information stays current. [0048]).
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 Kimmerling to include a final evaluation result for the digital human content including the scalar result value and the vector result value to identify another variation and update the final result while improving the accuracy of the image reality prediction model.
Regarding Claim 11, Kim discloses a computer-implemented method for evaluating a quality of digital human content included in a received source input, wherein the digital human content represents a computer-generated human, and the source input includes one or more of an image or a video containing the digital human content, the method comprising: receiving-and analyzing, by a parser of a test method selection unit, a test case input including identification information of a test case, a number of test methods, and, for each test method instance, a weight, a question list identifier indicating a question list used in the test case (In more detail of the sequential operations for the image reality prediction, a plurality of first test images are converted by using predetermined parameters affecting the image reality. Then, the converted first test images are displayed sequentially on a monitor used for the psychophysical observer test, which is subsequently applied to observers who are statistically classified into a similar group, and the test data are collected [0007]; [0038]-[0043]; [0047]), and an evaluation method; identifying, by a selector of the test method selection unit, from a pre-stored question list, a question item corresponding to the question list identifier, identifying the evaluation method by referring to a pre-stored evaluation method set, and selecting a test method by combining the identified question item with the identified evaluation method (At this time, the observer testing block 140 carries out the psychophysical observer test, i.e., evaluation method, by asking questions, i.e., pre-stored question list, after displaying one image on the display device and receiving answers, or by asking questions after displaying two images on the display device and receiving answers, i.e., [0036]); generating, by an evaluation result acquisition unit, a questionnaire based on the test method if the test method is a subjective test method, providing the generated questionnaire to external testers ([0038]-[0043]), and obtaining an evaluation score for the generated questionnaire computing, by a quality evaluation unit, a scalar result value and a vector result value by applying the weight included in the test case input to the evaluation score (a Z-score generating unit 152 for generating Z-score data, which are statistical analysis results, through using the sorted data inputted from the data sorting unit 151 [0049]), outputting a final evaluation result for the digital human content including the scalar result value and the vector result value (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 feeding back the output final evaluation result to the test method selection unit (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 test method represents information that queries a realism of the source input, wherein the final evaluation result includes a test case output (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 receiving-and analyzing, by a parser of a test method selection unit, a test case input including identification information of a test case, a number of test methods, and, for each test method instance, a weight, a question list identifier indicating a question list used in the test case, and an evaluation method; identifying, by a selector of the test method selection unit, from a pre-stored question list, a question item corresponding to the question list identifier, identifying the evaluation method by referring to a pre-stored evaluation method set, and selecting a test method by combining the identified question item with the identified evaluation method, and obtaining an evaluation score for the generated questionnaire computing, by a quality evaluation unit, a scalar result value and a vector result value by applying the weight included in the test case input to the evaluation score, outputting a final evaluation result for the digital human content including the scalar result value and the vector result value, and feeding back the output final evaluation result to the test method selection unit, wherein the test case output includes test information indicating a number of the testers, the scalar result value, the vector result value, and the test case input, wherein the test case input is updated based on the final evaluation result and a characteristic of the digital human content, and wherein the selecting selects the test method further based on the final evaluation result fed back from the quality evaluation unit.
Nevertheless, Kimmerling discloses receiving and analyzing, by a parser of a test method selection unit, a test case input including identification information of a test case, a number of test methods, and, for each test method instance, a weight, a question list identifier indicating a question list used in the test case, and an evaluation method (Biasing may be accomplished by weighing the group preference vector to reflect one person's misery being more important than another person misery. The item scores are calculated for each item based on the score from each individual (A and B). An even weight is determined by the formula W=100-min(100-scoreA, 100-scoreB) or, as described above, by the average of the scores [0090]), identifying, by a selector of the test method selection unit, from a pre-stored question list, a question item corresponding to the question list identifier, identifying the evaluation method by referring to a pre-stored evaluation method set and selecting a test method by combining the identified question item with the identified evaluation method (System 130 may include an API 132, an application program 134, and a database 140. Database 140 may include item preference data 158 and item scores 160 computed based on a user preference vector 170, which is computed by processing responses 120. Database 140 may also include a feature matrix 150 including item preference vectors 152 obtained from item preference data 158 [0042]), obtaining an evaluation score for the generated questionnaire computing, by a quality evaluation unit, a scalar result value and a vector result value by applying the weight included in the test case input to the evaluation score (System 130 may include an API 132, an application program 134, and a database 140. Database 140 may include item preference data 158 and item scores 160 computed based on a user preference vector 170, which is computed by processing responses 120. Database 140 may also include a feature matrix 150 including item preference vectors 152 obtained from item preference data 158 [0042]; A way to bias in favor of A is determined by the formula Wa=100-min(100-scoreA, (100-scoreB)*Scalar), where Scalar is less than 1 (for instance 0.5). Alternatively, a weight to bias in favor of A is determined by the formula Wa'=(scoreA+ScoreB*Scalar)/(1+Scalar), where Scalar is less than 1 (for instance 0.5) [0090]), wherein the test case output includes test information indicating a number of the testers, the scalar result value, the vector result value, and obtaining an evaluation score for the generated questionnaire computing, by a quality evaluation unit, a scalar result value and a vector result value by applying the weight included in the test case input to the evaluation score (System 130 may include an API 132, an application program 134, and a database 140. Database 140 may include item preference data 158 and item scores 160 computed based on a user preference vector 170, which is computed by processing responses 120. Database 140 may also include a feature matrix 150 including item preference vectors 152 obtained from item preference data 158 [0042]; A way to bias in favor of A is determined by the formula Wa=100-min(100-scoreA, (100-scoreB)*Scalar), where Scalar is less than 1 (for instance 0.5). Alternatively, a weight to bias in favor of A is determined by the formula Wa'=(scoreA+ScoreB*Scalar)/(1+Scalar), where Scalar is less than 1 (for instance 0.5) [0090]), wherein the test case output includes test information indicating a number of the testers, the scalar result value, the vector result value, and the test case input, wherein the test case input is updated based on the final evaluation result and a characteristic of the digital human content (System 130 may include an API 132, an application program 134, and a database 140. Database 140 may include item preference data 158 and item scores 160 computed based on a user preference vector 170, which is computed by processing responses 120. Database 140 may also include a feature matrix 150 including item preference vectors 152 obtained from item preference data 158 [0042]; A way to bias in favor of A is determined by the formula Wa=100-min(100-scoreA, (100-scoreB)*Scalar), where Scalar is less than 1 (for instance 0.5). Alternatively, a weight to bias in favor of A is determined by the formula Wa'=(scoreA+ScoreB*Scalar)/(1+Scalar), where Scalar is less than 1 (for instance 0.5) [0090]) wherein the test case input is updated based on the final evaluation result and a characteristic of the digital human content (In another variation, the item preference data is updated by folding-in additional items so that the system and method for eliciting information stays current. The item preference data is updated over time by folding-in additional items…In the case of movies, additional items may include movies not included in the item preference data, such as movies released after the last update of the item preference data [0048]), and wherein the selector of the test method selection unit is further configured to select the test method based on the final evaluation result fed back from the quality evaluation unit (In another variation, the item preference data is updated by folding-in additional items so that the system and method for eliciting information stays current. [0048]).
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 Kimmerling to include a final evaluation result for the digital human content including the scalar result value and the vector result value to identify another variation and update the final result while improving the accuracy of the image reality prediction model.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kim and Kimmerling, and further in view of Van Zon et al. (US20020090134) hereinafter referred to as ‘Van Zon’ and Zhang et al. (US20220292654) hereinafter referred to as ‘Zhang’.
Regarding Claim 8, Kim and Kimmerling disclose the claimed invention discussed in claim 1.
However, Kim does not explicitly disclose the evaluation result acquisition unit is configured to obtain, if the test method is an objective test method, an objective evaluation score for the test method using an external test tool.
Nevertheless, Van Zon discloses the evaluation result acquisition unit is configured to obtain, if the test method is an objective test method, an objective evaluation score (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 and Kimmerling with the teachings of Van Zon to provide a scalable objective metric while improving the accuracy of the image reality prediction model.
However, Kim, Kimmerling, and Van Zon do not explicitly disclose obtaining, by the evaluation result acquisition unit, an objective evaluation score for the test method using an external test tool if the test method is an objective test method.
Nevertheless, Zhang discloses an external test tool (To further illustrate the improvements of the image harmonization system 102 over conventional systems, researchers generated empirical metrics in addition to the generate image results of FIGS. 7 and 8. 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”), 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 Kimmerling, and Van Zon with the teachings of Zhang to include and external test tool both objective and subjective data while improving the accuracy of the image reality prediction model.
Regarding Claim 18, Kim and Kimmerling disclose the claimed invention discussed in claim 11.
However, Kim does not explicitly disclose obtaining, by the evaluation result acquisition unit, an objective evaluation score for the test method using an external test tool if the test method is an objective test method.
Nevertheless, Van Zon obtaining, by the evaluation result acquisition unit, an objective evaluation score for the test method (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 and Kimmerling with the teachings of Van Zon to include and external test tool both objective and subjective data while improving the accuracy of the image reality prediction model.
However, Kim, Kimmerling, and Van Zon do not explicitly disclose obtaining, by the evaluation result acquisition unit, an objective evaluation score for the test method using an external test tool if the test method is an objective test method.
Nevertheless, Zhang discloses an external test tool (To further illustrate the improvements of the image harmonization system 102 over conventional systems, researchers generated empirical metrics in addition to the generate image results of FIGS. 7 and 8. 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”), 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 Kimmerling, and Van Zon with the teachings of Zhang to include and external test tool both objective and subjective data while improving the accuracy of the image reality prediction model.
Response to Arguments
35 USC § 101
Applicant's arguments filed 06/23/2026 have been fully considered but they are not persuasive.
The Applicant argues (pg. 9): “The amended claims are not directed to a judicial exception under Prong One. The recited operations cannot, as a practical matter, be performed in the human mind. A parser that receives and analyzes a structured (e.g., JSON-formatted) test case input, a quality evaluation unit that computes both a scalar result value and a vector result value, and a feedback path that updates the test case input and re-drives a selector are computer-specific operations performed on machine-readable data and on a source input comprising an image or video. The recited characteristic of the digital human content is extracted from that image/video source input, not observed by a person. Operations that cannot practically be performed mentally are not mental processes. MPEP § 2106.04(a)(2)(ll).”
The Examiner respectfully disagrees and submits that “The amended claims are not directed to a judicial exception under Prong One. The recited operations cannot, as a practical matter, be performed in the human mind…” may include a machine/device that receives and analyzes input, however, a human is capable of identifying the information listed and selecting questions corresponding to the “question list identifier “ which still falls under mental process grouping.
Additionally, according to MPEP 2106.04(a)(2)(III), “ As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’)”.
The Applicant argues (pg. 9): “Nor are the claims 'directed to' mathematics merely because the quality evaluation unit performs a computation. A claim is not directed to a mathematical concept simply because it recites a calculation where, as here, the calculation is one step integrated into a larger practical architecture - parsing, dynamic selection, questionnaire generation, dual scalar/vector output, and closed-loop feedback. MPEP § 2106.04(a)(2)(1). The claims are directed to an improved quality-evaluation device, not to a mathematical relationship.”
The Examiner respectfully disagrees and submits “Nor are the claims 'directed to' mathematics merely because the quality evaluation unit performs a computation. A claim is not directed to a mathematical concept simply because it recites a calculation where, as here, the calculation is one step integrated into a larger practical architecture …” 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. 9):” Even assuming, arguendo, that an abstract idea can be identified, the amended claims integrate it into a practical application and are therefore eligible under Prong Two without reaching Step 2B. A claim integrates an exception into a practical application when the additional elements, considered individually and in combination, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field. MPEP § 2106.04(d)(1); § 2106.05(a). The amended claims do precisely that… This is not a result recited at a high level of generality; it is a particular way of configuring and operating the quality-evaluation device so that the device itself adapts the test methods it applies based on its own prior output and on machine-extracted characteristics of the content under evaluation”.
The Examiner respectfully disagrees and submits “…the amended claims integrate it into a practical application and are therefore eligible under Prong Two without reaching Step 2B…” that improvements in the abstract idea are not qualified improvements to demonstrate a practical application.
The Applicant argues (pg. 10): The Office discounted that contention on the ground that the then-pending limitations were treated as either an abstract idea or mere data gathering - an assessment that turned, in part, on the fact that the feedback-driven, dynamic-selection feature was not positively recited in the independent claims. That is no longer the case. The feedback-driven, characteristic-driven selection is now expressly recited in independent claims 1 and 11 as concrete additional elements that reconfigure how the quality-evaluation device operates. So recited, these elements are not mere data gathering; they reflect an improvement to the relevant technology and integrate any alleged exception into a practical application.
The Examiner respectfully disagrees and submits 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 and the “feeding the final evaluation result back to update… ” has been shown to be well-understood, routine, and conventional as discussed above in reference, Kimmerling. .
The Applicant argues (pg. 10): Even if the claims were found to recite an exception not integrated into a practical application, the additional elements - individually and as an ordered combination - amount to significantly more. The ordered combination of (i) parsing a structured test case input, (ii) selecting a test method by combining an identified question item with an identified evaluation method, (iii) generating a questionnaire and obtaining an evaluation score, (iv) computing both a scalar result value and a vector result value, and (v) feeding the final evaluation result back to update the test case input based on a characteristic of the digital human content so that the selector re-selects the applied test method, is not well-understood, routine, or conventional.
The Examiner respectfully disagrees and submits “Even if the claims were found to recite an exception not integrated into a practical application, the additional elements - individually and as an ordered combination - amount to significantly more…” that in order for the ordered combination to amount to significantly more the claims must include meaningful additional elements and the “feeding the final evaluation result back to update… ” has been shown to be well-understood, routine, and conventional as discussed above in reference, Kimmerling.
35 USC § 103
Applicant’s arguments with respect to claims 1, 8,11, and 18 have been considered but are moot in view of new grounds of rejection.
With regards to disclosing “the test case input is updated based on the final evaluation result and a characteristic of the digital human content, and wherein the selector of the test method selection unit is further configured to select the test method based on the final evaluation result fed back from the quality evaluation unit”.
The Examiner notes that Kim in combination with Kimmerling discloses “the test case input is updated based on the final evaluation result and a characteristic of the digital human content, and wherein the selector of the test method selection unit is further configured to select the test method based on the final evaluation result fed back from the quality evaluation unit” as discussed above in [0048].
Allowable Subject Matter
The following is an examiner’s statement of reasons for allowance:
Regarding Claims 1, 8, 11, and 18 the closest prior art Kim, Van Zon, and Zhang, either singularly or in combination, fail to anticipate or render obvious a quality evaluation unit configured to compute a scalar result value and a vector result value by applying the weight included in the test case input to the evaluation score, output a final evaluation result for the digital human content including the scalar result value and the vector result value, and feedback the output final evaluation result to the test method selection unit, in combination with all other limitations in the claim as claimed and defined by applicant.
Conclusion
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/SHARAH ZAAB/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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