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 .
This action is in response to the amendment filed on 11/28/2025. Claims 1-21 are pending in the case. This action is Final.
Applicant Response
In Applicant’s response dated 11/28/2025, Applicant amended Claims 2-6, added new claim 21 and argued against all objections and rejections previously set forth in the Office Action dated 08/27/2025.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea, without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-12 are directed to a computer-implemented method claim and claims 13-17 are directed to an a apparatus claim and Claim 18-20 are directed to a computer program claim. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).).
Regarding Claim 1,
At step 2A, Prong 1, Does the claim recite a judicial exception?
Claim 1 recites the steps of:
initializing, using at least one processor, a gaussian distribution with random parameters for each of creative scores, non-creative scores, and normal scores (This step for setting up probabilistic models using Gaussian distribution for different score categories and is mathematical concept and is understood to be a recitation of a mental process (i.e., math.).);
updating, using the at least one processor, the random parameters to maximize a likelihood of each sample score from a training set being from its corresponding gaussian distribution by using one or more gaussian mixture models with expectation maximization to cluster the creative scores, the non-creative scores, and the normal scores (This step for training step with gaussian mixture models (statistical Method) with expectation maximization( mathematical optimization) which falls under abstract mate mathematical operations)
estimating, using the at least one processor, a creativity score for each of a plurality of given samples as a probability of the corresponding given sample being in a creative cluster given one or more of the random parameters of the corresponding gaussian distribution (This step for computing a probability of cluster membership (a standard GMM output) and labeling it a “creative score” which is mathematical calculation and is understood to be a recitation of a mental process (i.e., math.).);
filtering, using the at least one processor, one or more of the plurality of samples based on the creativity scores to generate a set of optimal samples exhibiting creativity (This step for filtering based on computed metrics is data manipulation and is a mental process and is understood to be a recitation of a mental process (i.e., judgment or evaluation.).);
The claim recites a judicial exception, a mathematical concept applied in the field of machine learning. A person can mentally select a model based on certain selection criteria and filter/select/determine/modify the models (evaluation or judgement) which falls within the “Mental Processes” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application?
Further, the claim does not recite any additional element which could integrate this abstract idea into a practical application, because the additional elements recited of consist of:
a non-transitory memory; and one or more hardware processors coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to, that is, generic computer components on which to implement the abstract idea (see MPEP 2106.05(f)); retrieving attribute values corresponding to a set of attributes and associated with a transaction, that is, insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)), performing, for the transaction, an action based on the first output, which is merely “using a computer or other machinery” as a tool to perform the abstract idea step of generating an output (see MPEP 2106.05(f)).
The additional elements are recited at a high level of generality and do not amount to significantly more than the abstract idea (MPEP 2106.05(f)). Thus, the claim is directed towards the abstract idea.
Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
No, as shown above with respect to integration of the abstract idea into a practical application, the additional element of:
“a non-transitory memory; and one or more hardware processors coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to, that is, generic computer components on which to implement the abstract idea (see MPEP 2106.05(f));
retrieving attribute values corresponding to a set of attributes and associated with a transaction, that is, insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)), performing, for the transaction, an action based on the first output. The additional elements, alone and in combination, fail to integrate the abstract idea into a practical application.
wherein each sample is a fabric sample and each sample score is a fabric sample score. (field of use). The additional elements, alone and in combination, fail to integrate the abstract idea into a practical application.
Thus, the claims are not patent eligible. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Neither can insignificant extra-solution activity. All of these additional elements as generically claimed are thus considered well-understood, routine, and conventional. Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea.
Thus, these independent claims are not patent eligible.
The dependent claims respectively recite a judicial exception in limitations of: “wherein the estimating the creativity score is based on a distance metric between a subset scores distribution in an activation space and a pixel space across a set of samples; and wherein the filtering further comprises applying the distance metric to select one or more of the samples that are characterized by a higher probability of being creative.” (claims 2, 14 and 19), “wherein the estimating the creativity score further comprises analyzing a distribution of an activation space of a generative model under normal samples.”(claims 3), “wherein the training set is a set of samples related to a physical product and the method further comprises: transferring the optimal samples to a computer-aided design (CAD) device; creating additional creative designs based on the optimal samples; and applying the additional creative designs to a physical product.”(claims 4), “wherein the training set is a set of fabric design samples and the method further comprises: transferring the optimal samples to a computer-aided design (CAD) and fabric manufacturing system; and manufacturing fabric based on the optimal samples.”(claim 5), “wherein the estimating the creativity score further comprises: extracting activations from a creative decoder or generative adversarial network (GAN) for a set of latent vectors ;computing empirical p-values; computing a maximization of non-parametric scan statistics (NPSS); and estimating distributions of subset scores for creative and non-creative processes; and wherein the estimation of the creative score is performed using a subset of samples and a corresponding anomalous subset of nodes in a network.”)(claims 6, 15 and 20), “wherein the filtering further comprises discarding one of the given samples if activations are not scored under a threshold.”(claims 7), “wherein the estimating distributions of subset scores further comprises learning a plurality of gaussian functions by fitting scores corresponding to normal, non-creative and creative samples to individual clusters in conjunction with human evaluated information.”(Claim 8), “wherein the per-parameter neural network generates, for each parameter, an output that comprises (i) a direction for the parameter update for the parameter and (ii) a magnitude value for the parameter update for the parameter.”(claim 9 and 16), “comprising learning parameters of a gaussian mixture based on results of the estimating the distributions of the subset scores and using an iterative optimization technique.”(Claim 10), comprising providing the anomalous subset of nodes and the subset scanning scores to improve an explainability capability of the distance metric generated score of current variational autoencoders.”(claim 11), “wherein the initializing the gaussian distribution further comprises scanning a group-based subset using an iterative ascent procedure that alternates between a step of identifying a most anomalous subset of samples for a fixed subset of nodes, and a step that identifies the converse.”, (claim 12 and 12) and “wherein each sample is a fabric sample and each sample score is a fabric sample score.” (claim 21).
These additional limitations (in claims 2-12 and 14-17 and 19-21) also constitute concepts performed in the human mind which fall within the “Mental Processes” groupings of abstract ideas.
This judicial exception is not integrated into a practical application. Additional elements “computer readable medium comprising: computer program code (in claims 2-12 and 14-17 and 19-21), all amount to no more than adding insignificant extra-solution activity/specifications related to data gathering, data input, or data transmittal. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of non-transitory computer readable medium comprising: computer program code are again insignificant extra-solution activity steps that cannot provide an inventive concept. All of these additional elements as generically claimed are considered well-understood, routine, and conventional.
Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, all of the dependent claims are also not patent eligible.
7. Claims 18-20 are rejected under 35 U.S.C. 101 because the claimed is directed to non-statutory subject matter.
Specifically Claim 18 recites:
“… A computer program product for federated learning, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations comprising: ….”
The claim fails to clearly limit the computer readable storage medium to a Non- transitory form and the claim could be interpreted to be non-transitory medium or transitory medium since the specification is silent with respect to which the “computer readable storage medium” the system includes or excludes. As such, in a broadest reasonable interpretation, the claimed medium can include signal per se which is non-statutory. Examiner recommends that the claims be amended to “non-transitory computer readable storage medium” in order to overcome these 101 rejections.
Appropriate correction is required.
Examiner Comments
8. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 103
9. 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.
10. Claims 1,7-13 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Pani (US 20140156379 A1, 2014-06-05) in view of Sun (Pub. No US 20190266438 A1, Pub. Date: 2019-08-29.)
Regarding independent Claim 1,
Pani teaches a method comprising:
initializing, using at least one processor, a gaussian distribution with random parameters for each of creative scores, non-creative scores, and normal scores (see Pani: Fig.2, [0033], “input indicating a set of parameters may be received, for example, by receiving component 152 of scoring module 150. The set of parameters may be user configurable and may be received via a user interface, such as user interface 170. In such an embodiment, determining the creative quality scores, as described at block 240, may be based at least in part on the set of parameters. The parameters may include advertisement parameters and/or scoring parameters.”)
updating, using the at least one processor, the random parameters to maximize a likelihood of each sample score from a training set being from its corresponding gaussian distribution by using one or more gaussian mixture models with expectation maximization to cluster the creative scores, the non-creative scores, and the normal scores (see Pani: Fig.2, [0048], “a comparison of the creatives may take place after each scoring of the creatives, which may be periodic (e.g., once an hour, once a day, once a week, etc.). Note that one or more of blocks 200-260 may also be performed periodically such that updated determinations on making a creative active/inactive may be based on updated creative quality scores, which may be determined based on updated performance data.”)
estimating, using the at least one processor, a creativity score for each of a plurality of given samples as a probability of the corresponding given sample being in a creative cluster given one or more of the random parameters of the corresponding gaussian distribution (see Pani: Fig.2, [0043], “a creative quality score may be determined, by scoring component 158 of scoring module 150, for the online advertisement creatives. Such a determination may be based on the estimated performance values, which may include the propagated baseline estimates. In embodiments in which multiple performance values are also estimated, the determination of the creative quality scores may be based on the multiple estimated performance values. In one embodiment, the creative quality scores may include a confidence interval around some value. The value may be a mean or some other value and the confidence interval (CI) may be centered around that value.”)
Pani does not teach the method wherein:
filtering, using the at least one processor, one or more of the plurality of samples based on the creativity scores to generate a set of optimal samples exhibiting creativity.
However, Sun teaches the method wherein:
filtering, using the at least one processor, one or more of the plurality of samples based on the creativity scores to generate a set of optimal samples exhibiting creativity (see Sun: Fig.5B-5C, [0099], “. The patch match system can compare the plurality of target matching portions to neighboring pixels of the target patch and select one of the target matching portions based on the comparison. For example, in one or more embodiments, the patch match system selects that target matching portion with the highest similarity score.”)
Because both Pani and Sun are in the same/similar field of endeavor of creative quality scoring operation, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the system of Pani to include the system that filter samples based on the creativity scores to generate a set of optimal samples exhibiting creativity as taught by Sun. One would have been motivated to make such a combination in order to provide effective and accurate method of modifying digital images to correct inconsistencies. (see Sun [0005])
Regarding Claim 7,
As shown above, Pani and Sun teach all the limitation of Claim 1. Pani further teach the method wherein:
the filtering further comprises discarding one of the given samples if activations are not scored under a threshold 0 (see Pani: Fig.2, [0029] “thresholding may be the optimal novelty detector. Thus, the output of the discriminator for fake is the optimal novelty detector under the assumption that p.sub.g(x) is a mixture of novel and nominal data.”),
Regarding Claim 8,
As shown above, Pani and Sun teach all the limitation of Claim 1. Sun further teach the method wherein:
the estimating distributions of subset scores further comprises learning a plurality of gaussian functions by fitting scores corresponding to normal, non-creative and creative samples to individual clusters in conjunction with human evaluated information (see Pani: Fig.2, [0031], “a loss function may also be used for training the generator 106. For example, although in practice the generator 106 may never converge to the actual nominal data distribution, a loss function may nevertheless be included for the generator that encourages the generator to generate various novel images.”)
Regarding Claim 9,
As shown above, Pani and Sun teach all the limitation of Claim 8. Pani further teach the method wherein:
learning parameters of a gaussian mixture based on results of the estimating the distributions of the subset scores and using an iterative optimization technique (see Pani: Fig.2, [0059], “, the estimation process for estimating the CTR may consider the click generating process as a binomial random variable. For computational simplicity, the algorithm may estimate the binomial proportion (e.g., the CTR) by propagating the FFT under the assumption that the node state associated with this quantity is Gaussian distributed. )”
Regarding Claim 10,
As shown above, Pani and Sun teach all the limitation of Claim 1. Pani further teach the method wherein:
the iterative optimization technique is expectation maximization and, for a given sample, the creativity score is defined as a function of the probability of the given sample belonging to the creative cluster (see Pani: Fig.4, [0033], “user interface useable to display creative quality scores and/or continue or pause a creative. From left to right, the columns of the illustrated user interface display the creative, ad group, creative score, and recommendation, respectively. The row for creative 1 shows that creative 1 belongs to ad group 1 and has a creative score of 0.1 with a confidence interval around 0.1. Creatives 2 and 3 each also belong to ad group 1 and have creative scores of 0.5 and 0.4, respectively”)
Regarding Claim 11,
As shown above, Pani and Sun teach all the limitation of Claim 6. Pani further teach the method wherein:
providing the anomalous subset of nodes and the subset scanning scores to improve an explainability capability of the distance metric generated score of current variational autoencoders (see Pani: Fig.2, [0043], “a creative quality score may be determined, by scoring component 158 of scoring module 150, for the online advertisement creatives. Such a determination may be based on the estimated performance values, which may include the propagated baseline estimates. In embodiments in which multiple performance values are also estimated, the determination of the creative quality scores may be based on the multiple estimated performance values. In one embodiment, the creative quality scores may include a confidence interval around some value. The value may be a mean or some other value and the confidence interval (CI) may be centered around that value.”)
Regarding Claim 12,
As shown above, Pani and Sun teach all the limitation of Claim 6. Sun further teach the method wherein:
the initializing the gaussian distribution further comprises scanning a group-based subset using an iterative ascent procedure that alternates between a step of identifying a most anomalous subset of samples for a fixed subset of nodes, and a step that identifies the converse (see Pani: Fig.2, [0043], “a creative quality score may be determined, by scoring component 158 of scoring module 150, for the online advertisement creatives. Such a determination may be based on the estimated performance values, which may include the propagated baseline estimates. In embodiments in which multiple performance values are also estimated, the determination of the creative quality scores may be based on the multiple estimated performance values. In one embodiment, the creative quality scores may include a confidence interval around some value. The value may be a mean or some other value and the confidence interval (CI) may be centered around that value.”)
Regarding independent Claim 13,
Claim 13 is directed to an apparatus claim and has similar claim limitations as claim 1 and is rejected under the same rationale.
Regarding Claim 16,
Claim 16 is directed to an apparatus claim and has similar claim limitations as claim 9 and is rejected under the same rationale.
Regarding Claim 17,
Claim 17 is directed to an apparatus claim and has similar claim limitations as claim 12 and is rejected under the same rationale.
Regarding independent Claim 18,
Claim 18 is directed to computer program product and has similar claim limitations as claim 1 and is rejected under the same rationale.
10. Claims 21, 2-6, 14-15 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pani and Sun as applied to claims 1,7-13 and 16-18 as shown above and in further view of Wong (US 20200158659 A1, 2020-05-21)
Regarding Claim 21,
As shown above, Pani and Sun teach all the limitation of Claim 1. Pani and Sun does not explicitly teach the method wherein each sample is a fabric sample and each sample score is a fabric sample score.
However, Wong teaches the method wherein each sample is a fabric sample (see Wong: Fig.2, [0033], “Next, in step S102, according to rotation information of a roller motor of a cloth inspection machine, a camera is triggered to perform image acquisition of fabric to be detected. In step S103, based on an acquisition image, defects on the fabric to be detected are automatically identified. Based on the above method, the automatic detection and recognition of the defects of the fabric may be completed.”) and each sample score is a fabric sample score (see Wong: Fig.2, [0034], “The step S205 is proceeded directly to detect the size of the defect, and grade, score, etc., of the defect. It is worth mentioning that the grading and scoring of the defect may be performed according to characteristics of the defect. The characteristics of the defects includes but not limited to a length of the defect, a size of the defect region, a direction of the defect, a unit code length density at which the defect is occurred, etc. Measuring the size of the defect and evaluating grading of each of detected defects may further include that a type of defect is determined.”)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention to combine the statistical probabilistic modeling technique of Pani to include the output scoring, filtering and selection samples based on the scores of Sun and to apply such a combined technique to fabric sample data as taught by Wong. One would have been motivated to make such a combination in order to provide effective and accurate method of modifying digital images to correct inconsistencies and to provide improvement in detecting defect types in automated selection for fabric samples for quality enhancement.
Regarding Claim 2,
As shown above, Pani, Sun and Wong teach all the limitation of Claim 21. Sun further teach the method wherein:
the estimating the creativity score is based on a distance metric between a subset scores distribution in an activation space and a pixel space across a set of samples; and wherein the filtering further comprises applying the distance metric to select one or more of the samples that are characterized by a higher probability of being creative (see Sun: [0030], “the patch match system modifies the digital image by replacing a target region of the digital image with a target matching portion. To identify the target matching portion, the patch match system samples the digital image in accordance with the Gaussian mixture model. For instance, the patch match system can select a transformation that corresponds to a peak of the Gaussian mixture model and apply the selected transformation to the target region to identify a target matching portion of the digital image. Upon identifying a target matching portion, the patch match system can utilize the target matching portion to modify the digital image (e.g., fill the target region if the target matching portion satisfies a threshold similarity in relation to neighboring pixels).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention to combine the statistical probabilistic modeling technique of Pani to include the application of the distance metric to select one or more of the samples that are characterized by a higher probability of being creative as taught by Sun and to apply such a combined technique to fabric sample data as taught by Wong. One would have been motivated to make such a combination in order to provide effective and accurate method of modifying digital images to correct inconsistencies and to provide improvement in detecting defect types in automated selection for fabric samples for quality enhancement.
Regarding Claim 3,
As shown above, Pani, Sun and Wong teach all the limitation of Claim 21. Pani further teach the method wherein:
the estimating the creativity score further comprises analyzing a distribution of an activation space of a generative model under normal samples (see Pani: Fig.2, [0043], “, a creative quality score may be determined, by scoring component 158 of scoring module 150, for the online advertisement creatives. Such a determination may be based on the estimated performance values, which may include the propagated baseline estimates. In embodiments in which multiple performance values are also estimated, the determination of the creative quality scores may be based on the multiple estimated performance values.”
Regarding Claim 4,
As shown above, Pani, Sun and Wong teach all the limitation of Claim 21. Sun further teach the method wherein:
the training set is a set of samples related to a physical product and the method further comprises: transferring the optimal samples to a computer-aided design (CAD) device; creating additional creative designs based on the optimal samples; and applying the additional creative designs to a physical product (see Pani: Fig.6, [0103], “illustrates an original digital image 600a (e.g., the input digital image 100a) that the patch match system analyzes to generate a modified digital image 600b (e.g., the output digital image 100b). In particular, the patch match system analyzes the digital image 600a to generate a Gaussian mixture model in accordance with the disclosure above. For instance, the patch match system identifies sample patches, corresponding matching portions, and transformations between the sample patches and the corresponding matching portions. The patch match system then utilizes a Dirichlet process to generate a plurality of Gaussian distributions that represent the transformations.”)
Regarding Claim 5,
As shown above, Pani, Sun and Wong teach all the limitation of Claim 21. Sun further teach the method wherein:
the training set is a set of fabric design samples and the method further comprises: transferring the optimal samples to a computer-aided design (CAD) and fabric manufacturing system; and manufacturing fabric based on the optimal samples (see Wong: Fig.2, [0033], “automatically detecting fabric defect according to an embodiment of the present disclosure. As shown in the figure, the method mainly includes: step S101, a region of a fabric to be detected is preprocessed during a fabric transmission process by a noise removing device. Noise interference is eliminated during a detection process. Next, in step S102, according to rotation information of a roller motor of a cloth inspection machine, a camera is triggered to perform image acquisition of fabric to be detected; then,
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention to combine the statistical probabilistic modeling technique of Pani to include the application of the distance metric to select one or more of the samples that are characterized by a higher probability of being creative as taught by Sun and to apply transferring the optimal samples to a computer-aided design (CAD) and fabric manufacturing system; and manufacturing fabric based on the optimal samples as taught by Wong. One would have been motivated to make such a combination in order to provide effective and accurate method of modifying digital images to correct inconsistencies and to provide improvement in detecting defect types in automated selection for fabric samples for quality enhancement.
Regarding Claim 6,
As shown above, Pani, Sun and Wong teach all the limitation of Claim 21. Sun further teach the method wherein:
extracting activations from a creative decoder or generative adversarial network (GAN) for a set of latent vectors /; computing empirical p-values; computing a maximization of non-parametric scan statistics (NPSS); and estimating distributions of subset scores for creative and non-creative processes; and wherein the estimation of the creative score is performed using a subset of samples and a corresponding anomalous subset of nodes in a network (see Pani: Fig.2, [0024], “Generative Adversarial Networks may typically be used for generative modeling. For example, a GAN may include two competing differentiable functions that can be implemented using neural network models. One model, which may be called the generator 104 G(z; θ.sup.G), can map a noise sample z sampled from some prior distribution p(z) to the “fake” sample x=G(z; θ.sup.G); x should be similar to a “real” sample sampled from the nominal data distribution p.sub.data(x). The objective of the other model called the discriminator 108 D(x; θ.sup.D) may be to correctly distinguish generated samples from the training data which samples p.sub.data(x).”)
Regarding Claim 14,
Claim 14 is directed to an apparatus claim and has similar claim limitations as claim 2 and is rejected under the same rationale.
Regarding Claim 15,
Claim 15 is directed to an apparatus claim and has similar claim limitations as claim 6 and is rejected under the same rationale.
Regarding Claim 19,
Claim 19 is directed to computer program product and has similar claim limitations as claim 2 and is rejected under the same rationale.
Regarding Claim 20,
Claim 20 is directed to computer program product and has similar claim limitations as claim 6 and is rejected under the same rationale.
Response to Arguments
Claim Rejections - 35 U.S.C. § 101,
Regarding the 35 U.S.C. 101 rejection for claims 1-21 for being directed non-statutory subject matter has been sustained based on applicant amendments > examiner notes that the amendment to the claims that recite “wherein each sample is a fabric sample and each sample score is a fabric sample score” is Merly field of use limitation and the amended limitation does not provide any machine control or transformation of fabric or any improvement to computer functionality. Therefore, the 35 U.S.C. 101 rejection has been withdrawn.
Regarding the 35 U.S.C. 101 rejection for claims 18-20 because the claimed is directed to non-statutory subject matter has been sustained because the claims claim fails to clearly limit the computer readable storage medium to a Non- transitory form. Therefore, the 35 U.S.C. 101 rejection has been withdrawn.
Claim Rejections - 35 U.S.C. § 103,
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant argues the newly added claim limitation against the cited reference of Wong, however, the Lindsey has been cited to teach said limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PGPUB
NUMBER:
INVENTOR-INFORMATION:
TITLE / DESCRIPTION
US 20200218931 A1
Karlinsky; Leonid
Title: Representative-Based Metric Learning For Classification And Few-Shot Object Detection
Description: A method can include learning a common embedding space and a set of parameters for each one of a plurality of sets of mixture models, wherein one mixture model is associated with one class of objects within a set of object categories. The method can also include adding new mixture models to the set of mixture models to support novel categories based on a set of example embedding vectors computed for each one of the novel categories.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Zelalem Shalu
Examiner
Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145