DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is in response to the amendment filed 5 May 2026.
Claims 1-2, 4-5, 7, and 11-12 are pending. Claims 11-12 are newly added. Claims 3 and 6 are cancelled.
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 1-2, 4-5, 7, and 11-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
When considering subject matter eligibility under 35 USC 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1; MPEP 2106.03). If the claim falls within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed toward a judicial exception (Step 2A; MPEP 2106.04). This step is broken into two prongs.
The first prong (Step 2A, Prong 1) determines whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined at Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2; MPEP 2106.04). The second prong (Step 2A, Prong 2) determines whether the claims integrate the judicial exception into a practical application. If the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determine whether the claim is a patent-eligible exception (Step 2B; MPEP 2106.05).
If an abstract idea is present int the claim, in order to recite statutory subject matter, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application or amounts to significantly more than the abstract idea itself (see: 2019 PEG).
Step 1:
According to Step 1 of the two Step analysis, claims 1-2, 4, and 11-12 are directed toward a device (machine). Claim 5 is directed toward a method (process). Claim 7 is directed toward a calculator (machine). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 1, the claims recite:
convert a plurality of input samples, each of which is multidimensional data, into a plurality of feature vectors representing features of the plurality of input samples which is low-dimensional data, by using a plurality of feature calculation models (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation an evaluation of data to calculate a plurality of feature vectors representing features)
calculate similarity between an average value of the plurality of feature vectors and each representative vector corresponding to a class to which each of the plurality of input samples belongs, among a plurality of representative vectors, the plurality of representative vectors corresponding to a plurality of classes respectively and, having same dimensionality as each of the plurality of feature vectors (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation an evaluation of data to determine the similarity between an average value of the plurality of feature vectors and a representative vector of a class to which the input sample belongs)
calculate a diversity index value relates to a height of diversity of the plurality of feature vectors (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation is an evaluation of data to calculate a diversity index relating the diversity of the feature vectors)
the value is larger as the similarity between an average value of the plurality of feature vectors and the representative vector correspond to the class to which the input sample belongs is smaller, and the value is larger as the diversity index value is smaller (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation is a judgement of the evaluation function such that a value is larger as a similarity between an average value of the plurality feature vectors and the representative vector correspond to the class to which the sample belongs is smaller, and the value is larger as the diversity index is larger)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
at least one memory configured to store instructions
at least one processor configured to execute the instructions
These claim features are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Further, the claim discloses the additional elements:
each being formed by a neural network with two or more layers
a plurality of feature calculation models
learn parameters of the plurality of feature calculation models based on an evaluation function in which a value is larger as the similarity between the average value of the plurality of feature vectors and the each representative vector corresponding to the class to which each of the plurality of input samples belong is smaller
These elements are training elements recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
at least one memory configured to store instructions
at least one processor configured to execute the instructions
These claim features are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Further, the claim discloses the additional elements:
each being formed by a neural network with two or more layers
a plurality of feature calculation models
learn parameters of the plurality of feature calculation models based on an evaluation function in which a value is larger as the similarity between the average value of the plurality of feature vectors and the each representative vector corresponding to the class to which each of the plurality of input samples belong is smaller
These elements are training elements recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 2:
With respect to dependent claim 2, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
Claim 2 recites the elements:
calculate average similarity between each of the plurality of representative vectors and the plurality of feature vectors (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation is an evaluation of data to calculate the average similarity between the representative vectors and the feature vectors)
wherein, in the evaluation function, the value is larger as an error between a similarity vector that has the average similarity for each class as an element and a one-hot vector indicating a class to which the input sample belongs (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation is a judgement of the evaluation function such that a value is larger as an error between a similarity vector that has the average similarity for each class and a one-hot vector indicating a class to which the sample belongs)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 4:
With respect to dependent claim 4, the claim depends upon dependent claim 3. The analysis of claim 3 is incorporated herein by reference.
Step 2A, Prong 1:
Claim 4 recites the elements:
wherein the diversity index value is calculated by calculating a determinant of a product of a matrix in which a plurality of feature vectors are arranged and a transposed matrix of the matrix (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation is an evaluation of data to calculate a diversity index relating the diversity of the feature vectors)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 5:
With respect to independent claim 5, the claim recites the elements substantially similar to those in independent claim 1. Claim 5 is rejected under similar rationale.
Claim 7:
With respect to independent claim 7, the claim recites the elements substantially similar to those in independent claim 1. Claim 7 is rejected under similar rationale.
Claim 11:
With respect to claim 11, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
Claim 11 recites the elements:
the plurality of representative vectors have a predetermined distance between the plurality of representative vectors (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation is a judgement of the observation that the plurality of representative vectors have a predetermined distance between the plurality of representative vectors)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 12:
With respect to claim 12, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
Claim 12 recites the elements:
determine whether or not an input data is an adversarial sample based on the plurality of learned feature calculation models (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation is a judgement to determine whether the input data is an adversarial sample based on learned feature calculation models)
Step 2A, Prong 2:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim recites the additional element:
output the plurality of learned feature calculation models to an authentication device to use the authentication device
This limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
output the plurality of learned feature calculation models to an authentication device to use the authentication device
This limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1, 5, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (Mixture of deep CNN-based ensemble model for image retrieval, 2016) and further in view of Xia et al. (US 2020/0074275, published 5 March 2020, hereafter Xia) and further in view of Wu et al. (US 2019/0073590, published 7 March 2019, hereafter Wu) and further in view of Sandler et al. (US 11000135, filed 18 August 2017, hereafter Sandler).
As per independent claim 1, Huang discloses a learning device comprising:
calculate a plurality of feature vectors representing features of an input sample from the input sample which is multidimensional data by using a plurality of feature calculation models (Section III, subsection C: Here, multidimensional data in the form of an image is provided as input. A plurality of feature vectors of each image are extracted using a plurality of convolutional neural networks (CNNs) (Section II))
calculate a similarity between an average value of the plurality of feature vectors and a representative vector corresponding to a class to which the input sample belongs among a plurality of representative vectors to corresponding to a plurality of classes respectively (Section II, subsection B: Here, a similarity is calculated between an image and the ensemble models by averaging the results from each CNN)
learn parameters of the plurality of feature calculation models based on an evaluation function in which a value is larger as the similarity between the average value of the plurality of feature vectors and the representative vector corresponding to the class to which the input sample belongs is smaller (Section III, subsection B: Here, an ensemble model is trained to average predictions across multiple CNNs. The values are provided such that the higher the accuracy (larger the similarity) of the average value of the models the smaller the value because items are sorted in descending order)
Huang fails to specifically disclose:
at least one memory configured to store instructions
at least one processor configured to execute the instructions
convert a plurality of input samples, each of which is multidimensional data, into a plurality of feature vectors representing features of the plurality of input samples which is low-dimensional data, by using a plurality of feature calculation models, each being formed by a neural network with two or more layers
the representative vector having same dimensionality as each of the plurality of feature vectors
calculate a diversity index value related to a height of the diversity of the plurality of feature vectors
wherein, in the evaluation function, the value is larger as the similarity between the average value of the plurality of feature vectors and the representative vector corresponding to the class to which the input sample belongs is smaller, and the value is large as the diversity index value is smaller
However, Xia, which is analogous to the claimed invention because it is directed toward a machine learning system, discloses:
at least one memory configured to store instructions (Figure 8, item 804)
at least one processor configured to execute the instructions (Figure 8, item 802)
the representative vector having same dimensionality as each of the plurality of feature vectors (claim 7: Here, a sign function is applied to each value in a vector of feature values to generate a hash vector that has a same dimensionality as the feature vector)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Xia with Huang, with a reasonable expectation of success, as it would have allowed for ensuring a one to one correspondence between feature vectors and representative vectors to maintain corresponding comparisons between the vectors of the ensemble learning system.
Additionally, Wu discloses:
convert a plurality of input samples, each of which is multidimensional data, into a plurality of feature vectors representing features of the plurality of input samples which is low-dimensional data, by using a plurality of feature calculation models, each being formed by a neural network with two or more layers (paragraph 0079: Here, a preprocessing block converts the sparse inputs “Sparse-1” and “Sparse-2” into corresponding low-dimensional latent vector representations “InS1” and “InS2” by applying an embedding process using a neural network having a plurality of layers (Figures 1 and 2))
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Wu with Huang-Xia, with a reasonable expectation of success, as it would have allowed for converting sparse inputs into low-dimensional latent vector representations (Wu: paragraph 0079).
Additionally, Sandler discloses:
calculate a diversity index value related to a height of the diversity of the plurality of feature vectors (claim 1: Here, a machine learning system includes a diversity model configured to store a running index of items in a candidate pool and vector representations of the items. The size of the set is increased until a diversity measure is calculated based upon the diversity of the set)
wherein, in the evaluation function, the value is larger as the similarity between the average value of the plurality of feature vectors and the representative vector corresponding to the class to which the input sample belongs is smaller, and the value is large as the diversity index value is smaller (Abstract: Here, a classification is based upon relevance of the feature vectors to the representative vectors and a diversity of the sets of recommendations. The likelihood of the sample being properly classified increases as both the relevance and diversity increase).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combine Sandler with Huang-Xia, with a reasonable expectation of success, as it would have allowed for defining another ensemble model, diversity, for determining classification. This would have improved the robustness and confidence in classifying image data (Sandler: column 2, lines 39-50).
With respect to claims 5 and 7, the claims recite the limitations substantially similar to those in claim 1. Claims 5 and 7 are rejected under similar rationale.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Huang, Xia, Wu, and Sandler and further in view of Deng et al. (ArcFace for Disguised Face Recognition, 2019, hereafter Deng).
As per dependent claim 2, Huang, Xia, Wu, and Sandler disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Huang discloses instructions to calculate average similarity between each of the plurality of representative vectors and the plurality of feature vectors (Section II, subsection B: Here, a similarity is calculated between an image and the ensemble models by averaging the results from each CNN).
Huang fails to specifically disclose, wherein, in the evaluation function, the value is larger as an error between a similarity vector that has the average similarity for each class as an element and a one-hot vector indicating a class to which the input sample belongs.
However, Deng, which is analogous to the claimed invention because it is directed toward evaluating similarity between vectors, discloses in the evaluation function, the value is larger as an error between a similarity vector that has the average similarity for each class as an element and a one-hot vector indicating a class to which the input sample belongs (Section 4.2: Here, the value (weight) is larger for models associated with classes to which the element belongs, while those classes representing possible errors (low correlation) are assigned lower weights).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Dent with Huang-Xia, with a reasonable expectation of success, as it would have allowed for applying weights based upon the similarity of classification, thereby improving the classification of the ensemble model (Deng: Section 4.2).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Huang, Xia, Wu, and Sandler and further in view of Peran et al. (US 2020/0034776, published 30 January 2020, hereafter Peran).
As per dependent claim 4, Huang, Xia, Wu, and Sandler disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Sandler discloses calculating a diversity index value (Abstract). However, Huang fails to specifically disclose wherein the value is calculated by calculating a determinant of a product of a matrix in which a plurality of vectors are arranged and transposed matrix to matrix.
However, Peran discloses a value is calculated by calculating a determinant of a product of a matrix in which a plurality of feature vectors are arranged and transposed matrix to matrix (paragraph 0094: Here, the similarity between two sets is determined by calculating a product of a matrix in which a plurality of vectors are arranged and transposed matrix to matrix). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Peran with Huang-Xia-Sandler, with a reasonable expectation of success, as it would have allowed for determining the similarity between items for classifying based on clustering of adjacent items (Peran: paragraph 0003).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Huang, Xia, Wu, and Sandler and further in view of Rezaee (US 2018/0285438, published 4 October 2018, hereafter Rezaee).
As per dependent claim 11, Huang, Xia, Wu, and Sandler disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Huang fails to specifically disclose wherein the plurality of representative vectors have a predetermined distance between the plurality of representative vectors.
However, Rezaee, which is analogous to the claimed invention because it is directed toward identifying related contents, discloses wherein the plurality of representative vectors have a predetermined distance between the plurality of representative vectors (paragraph 0040: Here, a the vector representations are separated by a predetermined distance). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Rezaee with Huang-Xia-Wu-Sandler, with a reasonable expectation of success, as it would have allowed for clustering using vectors that are a predetermined distance separated to avoid overlap between classifying items (Rezaee: paragraph 0040).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Huang, Xia, Wu, and Sandler and further in view of Akimoto et al. (US 2023/0143808, filed 27 March 2020, hereafter Akimoto).
As per dependent claim 12, Huang, Xia, Wu, and Sandler disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Huang fails to specifically disclose wherein the at least one processor is further configured to execute the instructions to output the plurality of learned feature calculation models to an authentication device to cause the authentication device to determine whether or not an input data is an adversarial sample based on the plurality of learned feature calculation models.
However, Akimoto, which is analogous to the claimed invention because it is directed toward adversarial examples, discloses wherein the at least one processor is further configured to execute the instructions to output the plurality of learned feature calculation models to an authentication device to cause the authentication device to determine whether or not an input data is an adversarial sample based on the plurality of learned feature calculation models (Figure 4; paragraphs 0059-0063 and 0086-0087: Here, a feature vector is calculated and a similarity degree is calculated. From here, a similarity degree authorization threshold value is identified. If the threshold is met, the data is authorized and if not, the authorization fails). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Akimoto with Huang-Xia-Wu-Sandler, with a reasonable expectation of success, as it would have allowed for authorizing data only if it meets a similarity threshold (Akimoto: Figure 4).
Response to Arguments
Applicant’s arguments with respect to the rejection of claims under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Huang, Xia, Wu, Sandler.
Applicant's arguments with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
The applicant’s arguments appear to hinge on the belief that the “claims recite additional elements that integrate the alleged abstract idea into a practical application (page 7)” because the claim recites features directed to “improve performance and robustness of machine learning models faced with adversarial attacks (page 7).”
While the examiner appreciates these arguments, the examiner respectfully disagrees.
First it is important to note, the judicial exception alone cannot provide the improvement. However, the improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
In this instance, the following limitations are directed toward the abstract idea:
convert a plurality of input samples, each of which is multidimensional data, into a plurality of feature vectors representing features of the plurality of input samples which is low-dimensional data, by using a plurality of feature calculation models (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation an evaluation of data to calculate a plurality of feature vectors representing features)
calculate similarity between an average value of the plurality of feature vectors and each representative vector corresponding to a class to which each of the plurality of input samples belongs, among a plurality of representative vectors, the plurality of representative vectors corresponding to a plurality of classes respectively and, having same dimensionality as each of the plurality of feature vectors (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation an evaluation of data to determine the similarity between an average value of the plurality of feature vectors and a representative vector of a class to which the input sample belongs)
calculate a diversity index value relates to a height of diversity of the plurality of feature vectors (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation is an evaluation of data to calculate a diversity index relating the diversity of the feature vectors)
the value is larger as the similarity between an average value of the plurality of feature vectors and the representative vector correspond to the class to which the input sample belongs is smaller, and the value is larger as the diversity index value is smaller (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation is a judgement of the evaluation function such that a value is larger as a similarity between an average value of the plurality feature vectors and the representative vector correspond to the class to which the sample belongs is smaller, and the value is larger as the diversity index is larger)
The following additional elements are identified:
at least one memory configured to store instructions
at least one processor configured to execute the instructions
each being formed by a neural network with two or more layers
a plurality of feature calculation models
learn parameters of the plurality of feature calculation models based on an evaluation function in which a value is larger as the similarity between the average value of the plurality of feature vectors and the each representative vector corresponding to the class to which each of the plurality of input samples belong is smaller
As noted by the examiner, with respect to the following additional elements:
at least one memory configured to store instructions
at least one processor configured to execute the instructions
These claim features are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Further, with respect to the additional elements:
each being formed by a neural network with two or more layers
a plurality of feature calculation models
learn parameters of the plurality of feature calculation models based on an evaluation function in which a value is larger as the similarity between the average value of the plurality of feature vectors and the each representative vector corresponding to the class to which each of the plurality of input samples belong is smaller
These elements are training elements recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
For these reasons, this argument is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ikeda (US 2023/0216872): Discloses a neural network that converts feature vectors into low-dimensional vectors (paragraph 0104)
Wesolowski et al. (US 2019/0114537): Discloses a neural network that converts feature vectors into low-dimensional vectors (paragraph 0047)
Bai et al. (US 2009/0204605): Discloses a neural network that converts each word in a sentence into a low dimension vector that comprise feature representing the work (paragraph 0016)
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 KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R STORK/Primary Examiner, Art Unit 2128
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