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 .
The action is in response to the application filed on 10/25/2022. Claims 1-7 are pending and
have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/25/2022 is in compliance with the provisions of 37 CFR 1.97, 1.98, and MPEP § 609. It has been placed in the application file, and the information referred to therein has been considered as to the merits.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Objections
Claim 3 is objected to because of the following informalities: Claim 3, “a loss based on noise-contrastive estimation have symmetry” should be “loss based on noise-contrastive estimation having symmetry”.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Step 1:
The claim recites a non-transitory computer-readable recording medium, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea. Specifically, the limitation generating a first constraint condition used to maximize mutual information amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea generating a second constraint condition that reduces a distribution distance which amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea generating a third constraint condition that reduces a distribution distance… and increases distribution distance which amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea training a neural network that performs data classification, by performing optimization processing to solve an optimization problem based on the first constraint condition, the second constraint condition, and the third constraint condition which is a mathematical concept.
Step 2A prong 2:
The additional element of using a non-transitory computer-readable recording medium is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of acquiring a dataset without including correct answer data does not integrate the abstract idea into practical application because receiving graph data is considered an insignificant extra solution activity of “mere data gathering” MPEP 2106.05(g).
Step 2B:
The additional element of using a non-transitory computer-readable recording medium is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of acquiring a dataset without including correct answer data does not amount to significantly more because the additional element is an insignificant extra solution activity and further is a well understood routine and conventional activity. See MPEP 2106.05(d)(II)(i), (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
Therefore, the claim is ineligible.
Regarding Claim 2:
Claim 2 incorporates the rejection of Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the additional element generating a pair of data points estimated to have a same class label by defining a data point, starting from each of the data points, estimated to have a class label that is same as a class label of each of the datapoints by using a conversion function in Euclidean space is generally linked to the abstract idea MPEP 2106.05(h).
Regarding Claim 3:
Claim 3 which incorporates the rejection of Claim 1, recites a further abstract idea generating the third constraint condition by using a function that makes a loss based on noise-contrastive estimation have symmetry which is a mathematical concept. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 4:
Claim 4 incorporates the rejection of Claim 1. The claim further recites a description of performing the optimization processing in the training a neural network step and is ineligible for the same reasons as set forth in Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 5:
Claim 5 incorporates the rejection of Claim 1. The claim further recites a description of the distance distribution in the generating a second constraint condition and the generating a third constraint condition steps and is ineligible for the same reasons as set forth in Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 6:
Step 1:
The claim recites a method, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea. Specifically, the limitation generating a first constraint condition used to maximize mutual information amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea generating a second constraint condition that reduces a distribution distance which amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea generating a third constraint condition that reduces a distribution distance… and increases distribution distance which amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea training a neural network that performs data classification, by performing optimization processing to solve an optimization problem based on the first constraint condition, the second constraint condition, and the third constraint condition which is a mathematical concept.
Step 2A prong 2:
The additional element of using a computer is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of acquiring… a dataset without including correct answer data does not integrate the abstract idea into practical application because receiving graph data is considered an insignificant extra solution activity of “mere data gathering” MPEP 2106.05(g).
Step 2B:
The additional element of using a computer is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of acquiring… a dataset without including correct answer data does not amount to significantly more because the additional element is an insignificant extra solution activity and further is a well understood routine and conventional activity. See MPEP 2106.05(d)(II)(i), (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
Therefore, the claim is ineligible.
Regarding Claim 7:
Step 1:
The claim recites an information processing device, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea. Specifically, the limitation generate a first constraint condition used to maximize mutual information amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea generate a second constraint condition that reduces a distribution distance which amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea generate a third constraint condition that reduces a distribution distance… and increases distribution distance which amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea train a neural network that performs data classification, by performing optimization processing to solve an optimization problem based on the first constraint condition, the second constraint condition, and the third constraint condition which is a mathematical concept.
Step 2A prong 2:
The additional element of using a memory is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using a processor is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of acquire a dataset without including correct answer data does not integrate the abstract idea into practical application because receiving graph data is considered an insignificant extra solution activity of “mere data gathering” MPEP 2106.05(g).
Step 2B:
The additional element of using a memory is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using a processor is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of acquire a dataset without including correct answer data does not amount to significantly more because the additional element is an insignificant extra solution activity and further is a well understood routine and conventional activity. See MPEP 2106.05(d)(II)(i), (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
Therefore, the claim is ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4 and 6-7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hu et al. “SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification”, hereinafter “Hu”.
Regarding Claim 1, Hu teaches:
A non-transitory computer-readable recording medium storing a program for causing a computer to execute a process (The model in the SiMPLE algorithm is trained and tested, demonstrating that Hu performs their method on a computer, in which processor, memory, and storage devices are inherent, p. 7, col. 1, ¶2, “For CIFAR-10, we set λU = 75 and λP = 75; we set λU = λP = 250 for SVHN. For both datasets, we use SGD with cosine learning rate decay”, p. 5-6, Tables 1-3 showing accuracy of SimPLE), the process comprising:
acquiring a dataset without including correct answer data (p. 3, col. 2, ¶3, “a batch of unlabeled data”);
generating a first constraint condition used to maximize mutual information between each of data points included in the dataset and a class label assigned to the each of the data points (Unsupervised loss maximizes mutual information, p. 2, Figure 2 description, “unsupervised loss LU that aligns the strongly augmented unlabeled data with pseudo labels generated from weakly augmented data”);
generating a second constraint condition that reduces a distribution distance regarding class labels assigned to two data points between which a Euclidean distance is closer than a predetermined value (p. 2, Figure 2 description, “Pair Loss LP that minimizes the statistical distance between predictions of strongly augmented data, based on the similarity and confidence of their pseudo labels”, predetermined value is confidence threshold, p. 5, col. 1, Figure 3 description, “any pseudo label and image tuple qr (light blue) and vr (dark blue), if the overlapping proportion (i.e. similarity) between ql and qr is greater than the confidence threshold τs, this tuple (qr, vr) will contribute toward the Pair Loss”);
generating a third constraint condition that reduces a distribution distance regarding class labels that are assigned to respective data points estimated to have a same class label , and increases a distribution distance regarding class labels that are assigned to respective data points estimated to have different class labels (p. 5, Figure 3 description, “Pair Loss by pushing model’s prediction of a strongly augmented version of vr to the “anchor” ql”, reducing distribution distance of data points with same pseudo label will increase distribution distance of data points with different pseudo labels); and
training a neural network that performs data classification, by performing optimization processing to solve an optimization problem based on the first constraint condition, the second constraint condition, and the third constraint condition (p. 5, col. 2, ¶3, “we use Wide ResNet 28-2 [33] as our backbone”, p. 5, col. 2, ¶2, “By putting together all the components introduced in this section, we now present the SimPLE algorithm. During training, for a mini-batch of samples, SimPLE… we optimize the loss terms based on augmented samples and pseudo labels”, p. 5-6, Tables 1-3 show classification accuracy after training).
Regarding Claim 2, Hu teaches the non-transitory computer-readable recording medium of Claim 1 as referenced above. Hu further teaches:
the process further comprising: generating a pair of data points estimated to have a same class label by defining a data point (p. 4, col. 2, ¶2, “we use a high confidence pseudo label of an unlabeled point, p, as an “anchor”. All unlabeled samples whose pseudo labels are similar enough to p need to align their predictions under severe perturbation to the “anchor.””), starting from each of the data points, estimated to have a class label that is same as a class label of the each of the data points by using a conversion function in a Euclidean space (Average of the model’s predictions is in Euclidean space, sharpening operation is conversion function, p. 3, col. 2, ¶5, “first take the average of the model’s predictions of several weakly augmented versions of the same unlabeled sample as its pseudo label… we use the sharpening operation defined in [2] to increase the temperature of the label’s distribution… the network will push this sample further away from the decision boundary”, p. 4, Algorithm 1, lines 10-11).
Regarding Claim 3, Hu teaches the non-transitory computer-readable recording medium of Claim 1 as referenced above. Hu further teaches:
the process further comprising: generating the third constraint condition by using a function that makes a loss based on noise-contrastive estimation have symmetry (Pair loss based on ql and qr which are created with random noise and contrasts them, “if the overlapping proportion… between ql and qr… (qr, vr) will contribute toward the Pair Loss”, Equations 6 and 7 Fsim(ql, qr) is symmetric).
Regarding Claim 4, Hu teaches the non-transitory computer-readable recording medium of Claim 1 as referenced above. Hu further teaches:
the process further comprising: performing the optimization processing so as to satisfy the first constraint condition after satisfying the second constraint condition and the third constraint condition (The second and third condition can be satisfied before first condition because for overall loss the order of calculating unsupervised loss and pair loss does not matter, p. 4, col. 1, Equation 3 showing overall loss using both unsupervised loss and pair loss, computation of unsupervised loss and pair loss have no dependencies on each other, p. 4, col. 1, Equation 5 showing unsupervised loss, p. 4, col. 2, Equation 6 showing pair loss).
Regarding Claim 6, Hu teaches:
An information processing method, comprising:
acquiring, by a computer (The model in the SiMPLE algorithm is trained and tested, demonstrating that Hu performs their method on a computer, in which processor, memory, and storage devices are inherent, p. 7, col. 1, ¶2, “For CIFAR-10, we set λU = 75 and λP = 75; we set λU = λP = 250 for SVHN. For both datasets, we use SGD with cosine learning rate decay”, p. 5-6, Tables 1-3 showing accuracy of SimPLE), a dataset without including correct answer data (p. 3, col. 2, ¶3, “a batch of unlabeled data”);
generating a first constraint condition used to maximize mutual information between each of data points included in the dataset and a class label assigned to the each of the data points (Unsupervised loss maximizes mutual information, p. 2, Figure 2 description, “unsupervised loss LU that aligns the strongly augmented unlabeled data with pseudo labels generated from weakly augmented data”);
generating a second constraint condition that reduces a distribution distance regarding class labels assigned to two data points between which a Euclidean distance is closer than a predetermined value (p. 2, Figure 2 description, “Pair Loss LP that minimizes the statistical distance between predictions of strongly augmented data, based on the similarity and confidence of their pseudo labels”, predetermined value is confidence threshold, p. 5, col. 1, Figure 3 description, “any pseudo label and image tuple qr (light blue) and vr (dark blue), if the overlapping proportion (i.e. similarity) between ql and qr is greater than the confidence threshold τs, this tuple (qr, vr) will contribute toward the Pair Loss”);
generating a third constraint condition that reduces a distribution distance regarding class labels that are assigned to respective data points estimated to have a same class label , and increases a distribution distance regarding class labels that are assigned to respective data points estimated to have different class labels (p. 5, Figure 3 description, “Pair Loss by pushing model’s prediction of a strongly augmented version of vr to the “anchor” ql”, reducing distribution distance of data points with same pseudo label will increase distance of data points with different pseudo labels); and
training a neural network that performs data classification, by performing optimization processing to solve an optimization problem based on the first constraint condition, the second constraint condition, and the third constraint condition (p. 5, col. 2, ¶3, “we use Wide ResNet 28-2 [33] as our backbone”, p. 5, col. 2, ¶2, “By putting together all the components introduced in this section, we now present the SimPLE algorithm. During training, for a mini-batch of samples, SimPLE… we optimize the loss terms based on augmented samples and pseudo labels”, p. 5-6, Tables 1-3 show classification accuracy after training).
Regarding Claim 7, Hu teaches:
An information processing device, comprising: a memory; and a processor coupled to the memory and the processor configured to (The model in the SiMPLE algorithm is trained and tested, demonstrating that Hu performs their method on a computer, in which processor, memory, and storage devices are inherent, p. 7, col. 1, ¶2, “For CIFAR-10, we set λU = 75 and λP = 75; we set λU = λP = 250 for SVHN. For both datasets, we use SGD with cosine learning rate decay”, p. 5-6, Tables 1-3 showing accuracy of SimPLE):
acquire a dataset without including correct answer data (p. 3, col. 2, ¶3, “a batch of unlabeled data”);
generate a first constraint condition used to maximize mutual information between each of data points included in the dataset and a class label assigned to the each of the data points (Unsupervised loss maximizes mutual information, p. 2, Figure 2 description, “unsupervised loss LU that aligns the strongly augmented unlabeled data with pseudo labels generated from weakly augmented data”);
generate a second constraint condition that reduces a distribution distance regarding class labels assigned to two data points between which a Euclidean distance is closer than a predetermined value (p. 2, Figure 2 description, “Pair Loss LP that minimizes the statistical distance between predictions of strongly augmented data, based on the similarity and confidence of their pseudo labels”, predetermined value is confidence threshold, p. 5, col. 1, Figure 3 description, “any pseudo label and image tuple qr (light blue) and vr (dark blue), if the overlapping proportion (i.e. similarity) between ql and qr is greater than the confidence threshold τs, this tuple (qr, vr) will contribute toward the Pair Loss”);
generate a third constraint condition that reduces a distribution distance regarding class labels that are assigned to respective data points estimated to have a same class label, and increases a distribution distance regarding class labels that are assigned to respective data points estimated to have different class labels(p. 5, Figure 3 description, “Pair Loss by pushing model’s prediction of a strongly augmented version of vr to the “anchor” ql”, reducing distribution distance of data points with same pseudo label will increase distance of data points with different pseudo labels); and
train a neural network that performs data classification, by performing optimization processing to solve an optimization problem based on the first constraint condition, the second constraint condition, and the third constraint condition (p. 5, col. 2, ¶3, “we use Wide ResNet 28-2 [33] as our backbone”, p. 5, col. 2, ¶2, “By putting together all the components introduced in this section, we now present the SimPLE algorithm. During training, for a mini-batch of samples, SimPLE… we optimize the loss terms based on augmented samples and pseudo labels”, p. 5-6, Tables 1-3 show classification accuracy after training).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Hu in view of Brownlee “How to Calculate the KL Divergence for Machine Learning”, hereinafter Brownlee.
Regarding Claim 5, Hu teaches the non-transitory computer-readable recording medium of Claim 1 as referenced above. Hu does not teach, but Brownlee teaches:
wherein the distribution distance is a Kullback Leibler (KL) distance (Brownlee, p. 2, Kullback-Leibler Divergence, ¶4, “KL divergence can be calculated as the negative sum of probability of each event in P multiplied by the log of the probability of the event in Q over the probability of the event in P. KL(P || Q) = – sum x in X P(x) * log(Q(x) / P(x))”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the teachings of Hu calculating distance for pair wise loss and the KL divergence of Brownlee. The modification would have been motivated to measure how much one distribution differs from another (Brownlee, p. 2, Statistical Distance, ¶4-5, “One approach is to calculate a distance measure between the two distributions. This can be challenging as it can be difficult to interpret the measure. Instead, it is more common to calculate a divergence between two probability distributions”, p. 2, Kullback-Leibler Divergence, ¶1, “The Kullback-Leibler Divergence score, or KL divergence score, quantifies how much one probability distribution differs from another probability distribution.”).
Conclusion
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/JESSE C COULSON/
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122