Detailed Action
This action is responsive to Applicant’s amendment/response filed 09/09/2025 for application 17/651,961, in which:
Claims 1, 9, and 15 are independent claims.
Claims 1-5, 9-12, and 15-18 are currently amended.
Claims 1-20 are currently pending.
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 .
Regarding the 35 U.S.C. §101 Rejections:
Applicant's amendments to Claims 9-14 overcomes the ineligibility due to being directed to non-statutory subject patter (as the claims were directed to signals per se). However, the Claims are still rejected due to being subject-matter ineligible; more explanations recited below within the responses and in the office action.
Response to Arguments
Applicant's arguments filed 09/09/2025 have been fully considered but they are not persuasive.
Regarding the Objection to the Title:
Applicant asserts (Page 17), that the Title of the Invention has been amended to ensure that it is descriptive; thus, requesting the objection to be withdrawn.
Examiner respectfully disagrees. Applicant's amendments to the title of the invention does not overcome the objection due to the title not being clearly indicative of the invention which the claims are directed. The current title being ‘NEURAL NETWORK LEARNING METHOD, COMPUTER PROGRAM PRODUCT, AND LEARNING APPARTUS’ is a generic title that can be applicable for any learning method that utilizes a neural network for a learning method. The objection is still maintained as the title is merely reciting statutory categories with little to no descriptors.
Regarding the 35 U.S.C. §101 Rejections (Continued):
Applicant's arguments regarding the 35 U.S.C. § 101 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant disagrees and traverses the rejections of claims 1-20 (Page 17-21), as the claims have been amended with respects to 35 U.S.C. §101. Applicant further support their assertion by noting that the amended claims do not recite a judicial exception. Applicant further support their assertion by noting Example 39 of the 2019 PEG Examples; where the training … limitation within the example does not recite any judicial exception. Similarly, amended claim 1 recites the performing limitation … is like Example 39 and unlike Example 47 (as it does not specify any mathematical concepts). When considering amended claim 1 holistically, the claim clearly shows a technical improvement to the learning of a neural network and thus integrates the abstract idea into a practical application.
Examiner respectfully disagrees. For the reasons given below and in the rejections under 35 U.S.C. § 101 below, the claims are directed to an abstract idea (Step 2A Prong 1) and do not integrate the abstract idea into a practical application (Step 2A Prong 2). The pending claims recite abstract ideas that fall in at least one of the permissible groups, and noted within the office action below in more details. The independent claims fail to recite the steps that achieve the improvement. The independent claims are no more detailed than reduce a value of a loss that describes a correlation with specific representations with no steps on how to achieve an improvement; thus, the Claims are not a technical solution to a technical problem. Examiner agrees that the training … limitation does not recite a mental process and is evaluated as an additional element within Example 39; however, amended Claim 1 recites performing learning of a neural network so as to reduce a value of a first loss … the neural network is being utilized to perform a mathematical concept which is merely a mathematical relationship between variables and/or numbers using a mathematical formula (reducing a first loss value/function). The rejections have been updated below, the rejection to all Claims (including Claim 1, analogous independent Claims, and all dependent Claims) are maintained and updated as necessitated by Claim amendments. Applicant's arguments are not persuasive.
Applicant asserts (Page 21-22), the amended claims recite significantly more. The applicant supports their assertions by noting that … output from at least one of intermediate layers and a final layer in the neural network to which a training data group including a plurality of pieces of training data has been input… is merely not "receiving or transmitting data over a network" but is instead a particular modifier of a structural element. Nevertheless, the independent claims have been updated to recite … at least one of intermediate layers and a final layer in the neural network which recites significantly more than any judicial exception.
Examiner respectfully disagrees. Limitations within Step 2A Prong 2 are unable to integrate the abstract idea(s) into a practical application as the additional elements do not amount to significantly more than the judicial exception as the additional elements are merely performing the abstract idea(s) where the process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, restricting the abstract idea to a Particular Technological Environment, or insignificant extra-solution activity of data gathering. Performance “on a computer” or “with a machine learning model” or “using a specialized machine” does not prevent a limitation from being practically performable. Additionally, the Claims do not reflect any improvement in the functioning of a computer or hardware processor rather the additional elements merely use a generic computer component to perform the abstract idea and/or restrict the abstract idea to a specific technological environment. Therefore, the claims do not integrate the judicial exception into a practical application nor amount to significantly more. The claim is not patent eligible. Although the Claims are interpreted in light of the specification, limitations from the specification are not read into the Claims.
MPEP 2106.05(a) recites:
After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology … the claim must include the components or steps of the invention that provide the improvement described in the specification
…
It is important to note, the judicial exception alone cannot provide the improvement. 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.
Applicant fails to show how any alleged technical improvement would be provided by anything more than the judicial exception on its own. Additionally, applicant fails to show how the claim includes components or steps that would provide the alleged improvement described in the specification. The independent claims are no more detailed than reduce a value of a loss that describes a correlation with specific representations with no steps on how to achieve an improvement. By MPEP 2106.05(f)(1), "the claim recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished". Moreover, the examiner maintains that the Claim does not impose any meaningful limits on the judicial exception. As noted in the rejection, the Claim does not include additional elements that are sufficient to amount to an integration of the identified abstract idea into a practical application, thus the claim is directed to an abstract idea. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1 and all dependent Claims) are maintained and updated as necessitated by Claim amendments. More specific details are discussed below within the 35 USC § 101 Rejections.
Applicant asserts (Pages 22-23), as claim 1 is patent-eligible, similar independent claims (9 and 15), and all the dependent claims should be considered patent-eligible due to similarity and dependency, respectively.
Examiner respectfully disagrees. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive.
Regarding the 35 U.S.C. §102 Rejections:
Applicant's arguments regarding the 35 U.S.C. § 102 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant traverses the 35 U.S.C. § 102 rejections (Pages 23-25), but nevertheless amends the claims to further distinguish from Wu. Applicant further supports their assertions by noting that Wu does not explicitly disclose the representing a correlation between channels … limitation; where the objective loss within Equation 17 of Wu is different from the first loss of the claimed invention as the correlations represent different values (where the correlation is noted within the specification which details the feature space).
Examiner respectfully disagrees. Wu discloses Equation 17 which is represented in Figure 2 to denote the overall objective loss which represents a correlation between channels; where channels are being interpreted by the examiner as output activations of the layer. Although the Claims are interpreted in light of the specification, limitations from the specification are not read into the Claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The objective loss is indeed different than the cited specification; however, the claim does not recite the details that differentiate the correlation from the specification and the prior art reference. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1 and all dependent Claims) are maintained and updated as necessitated by Claim amendments.
Applicant further asserts (Pages 25-26 ), that Wu focuses on the similarity between individual features (Equation 17: f(xi), f(xy), f(xz)) where the claimed invention is an entirety of the feature vectors denoted by Fig 2B: 40. Also, Wu is silent on reducing a value of first loss … limitation; thus, Claim 1, similar independent claims, and pending dependent claims should be reconsidered under 102 and the rejections should be withdrawn.
Examiner respectfully disagrees. Wu does utilize the feature vector outputs (f(xi), f(xy), f(xz)) to represent the feature outputs from each activation channel output (as shown within Fig. 2). Wu explicitly discloses an overall objective loss which is used for reducing a value of first loss and is not completely silent on the limitation. More details within the office action. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive.
Regarding the 35 U.S.C. §103 Rejections:
Applicant's arguments regarding the 35 U.S.C. § 103 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant traverses the 35 U.S.C. § 103 rejections (Pages 26-27), as Wu fails to disclose the performing learning … limitation noted within the 102 rejection which is also not cured by Park. Thus, the applicant requests that claim 1, similar independent claims, and all dependent claims should be reconsidered and the rejections should be withdrawn.
Examiner respectfully disagrees. As stated above, Wu teaches the performing learning … limitation which does not need to be cured by Park. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1 and all dependent Claims) are maintained and updated as necessitated by Claim amendments. More specific details are discussed below within the 35 USC § 103 Rejections.
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.
The following title is suggested: ‘ADVERSARIAL LEARNING METHOD, COMPUTER PROGRAM PRODUCT, AND LEARNING APPARATUS TO REDUCE CORRELATION LOSS BETWEEN CHANNELS’.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 1 further recites the method comprising of performing learning of a neural network so as to reduce a value of a first loss representing a correlation between channels in feature vectors output from at least one of intermediate layers and a final layer in the neural network … (which is a mathematical relationship between variables and/or numbers using a mathematical formula). Claim 1 thus recites an abstract idea (that falls into the “mathematical concepts” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
A learning method to be performed by a computer, the learning method comprising (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
… to which a training data group including a plurality of pieces of training data has been input (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
wherein the feature vectors are represented by channel values of the channels, for each of the plurality of pieces of training data (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h))
the correlation between the channels is a value that is included in the feature vectors and that represents a correlation between groups of the channel values of the training data group, for the channels that are different from each other (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional element a is merely applying the abstract idea on a computer (MPEP 2106.05(f)) which cannot provide significantly more. Additional element b falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Additional elements c and d are only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 2 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 2 further recites the method comprising of:
deriving a value of a second loss and a value of a third loss , the value of the second loss being derived based on the first feature vector and representing a correlation between the annotation information given to the supervised training data set and output information … , the output information corresponding to the annotation information, the value of the third loss being a value of the first loss representing a correlation between channels in the second feature vectors … (which is a mathematical relationship between variables and/or numbers using a mathematical formula)
… the learning of the neural network is performed so as to reduce the value of the second loss and the value of the third loss (which is a mathematical relationship between variables and/or numbers using a mathematical formula)
Claim 2 thus recites an abstract idea (that falls into the “mathematical concepts” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
inputting a supervised training data set and an unsupervised training data set to the neural network as the plurality of pieces of training data, the supervised training data set including a plurality of pieces of supervised training data given annotation information, the unsupervised training data set including a plurality of pieces of unsupervised training data not given the annotation information (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
acquiring first feature vectors and second feature vectors, the first feature vectors being the feature vectors output from the neural network by inputting the supervised training data set, the second feature vectors being feature vectors output from the neural network by inputting the unsupervised training data set (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
… obtained from the neural network by inputting the supervised training data set … (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional elements a-c fall within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Thus, the claim is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 3 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 3 further recites the method comprising of:
deriving a value of a second loss , a value of a fourth loss , and a value of a third loss , the value of a second loss being derived based on the first feature vectors and representing a correlation between the annotation information given to the supervised training data set and output information … , the output information corresponding to the annotation information, the value of the fourth loss being a value of the first loss representing a correlation between channels in the first feature vectors, the value of the third loss being a value of the first loss representing a correlation between channels in the second feature vectors … (which is a mathematical relationship between variables and/or numbers using a mathematical formula)
… the learning of the neural network is performed so as to reduce the value of the second loss , the value of the third loss , and the value of the fourth loss (which is a mathematical relationship between variables and/or numbers using a mathematical formula)
Claim 3 thus recites an abstract idea (that falls into the “mathematical concepts” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
inputting a supervised training data set and an unsupervised training data set to the neural network as the plurality of pieces of training data, the supervised training data set including a plurality of pieces of supervised training data given annotation information, the unsupervised training data set including a plurality of pieces of unsupervised training data not given the annotation information (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
acquiring first feature vectors that are the feature vectors output from the neural network by inputting the supervised training data set and a second feature vectors that are the feature vectors output from the neural network by inputting the unsupervised training data set (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
… obtained from the neural network by inputting the supervised training data set … (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional elements a-c fall within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Thus, the claim is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 4 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 4 further recites the method comprising of:
deriving a value of a second loss and a value of a fourth loss , the value of the second loss being derived based on the first feature vectors and representing a correlation between the annotation information given to the supervised training data set and output information … , the output information corresponding to the annotation information, the value of the fourth loss being a value of the first loss representing a correlation between channels in the first feature vectors … (which is a mathematical relationship between variables and/or numbers using a mathematical formula)
… the learning of the neural network is performed so as to reduce the value of second loss and the value of fourth loss
Claim 4 thus recites an abstract idea (that falls into the “mathematical concepts” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
inputting a supervised training data set to the neural network as the plurality of pieces of training data, the supervised training data set including a plurality of pieces of supervised training data given annotation information (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
acquiring first feature vectors that are the feature vectors output from the neural network by inputting the supervised training data set (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
… obtained from the neural network by inputting the supervised training data set … (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional elements a-c fall within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Thus, the claim is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 5 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 5 further recites the method comprising of wherein a correlation coefficient is used for calculation of the value of the first loss (which is a mathematical relationship between variables and/or numbers using a mathematical formula). Claim 5 thus recites an abstract idea (that falls into the “mathematical concepts” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 6 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 6 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 6 thus recites an abstract idea (that falls into the “mathematical concepts” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element consists wherein the plurality of pieces of training data includes a plurality of groups each including a plurality of supervised training data sets and a plurality of groups each including a plurality of unsupervised training data sets (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Thus, the claim is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 7 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 7 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 7 thus recites an abstract idea (that falls into the “mathematical concepts” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
receiving an input of a learning condition including at least one of a network structure of the neural network as a target of the learning, the training data to be used in the learning, and a description of setting to be used at a time of the learning, (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
wherein at the performing the learning, the learning of the neural network is performed in accordance with the learning condition having been received (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional element a falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Additional element b is merely applying the abstract idea on a computer (MPEP 2106.05(f)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 8 recites the method of Claim 7. Claim 7 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 8 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 7. Claim 8 thus recites an abstract idea (that falls into the “mathematical concepts” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element consists displaying a display screen including at least one of a learning progress state of the neural network and a content of change recommendation for the learning condition depending on the learning progress state (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of presenting offers and gathering statistics (MPEP 2106.05(d)(II): OIP Techs., 788 F.3d at 1362- 63, 115 USPQ2d at 1092-93). Thus, the claim is subject-matter ineligible.
Regarding Claims 9-14:
Claims 9-14 incorporate substantively all the limitations of Claims 1-4 and 7-8 in a computer program product (thus, a manufacture) and further recites comprising a computer-readable medium including programmed instructions, the instructions causing a computer to execute (these claim limitations appear to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f)) and does not appear to integrate the abstract idea into a particular application; thus, the claim is subject-matter ineligible as it does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself); thus, Claims 9-14 are rejected for reasons set forth in the rejections of Claims 1-4 and 7-8, respectively.
Regarding Claims 15-20:
Claims 15-20 incorporate substantively all the limitations of Claims 1-4 and 7-8 in a learning apparatus (thus, a system) and further recites comprising one or more hardware processors configured to (these claim limitations appear to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f)) and does not appear to integrate the abstract idea into a particular application; thus, the claim is subject-matter ineligible as it does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself); thus, Claims 15-20 are rejected for reasons set forth in the rejections of Claims 1-4 and 7-8, respectively.
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.
Claims 1-7, 9-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al., “Weakly Semi-Supervised Deep Learning for Multi-Label Image Annotation” in view of Cogswell et al., “Reducing Overfitting in Deep Networks by Decorrelating representations”.
Regarding Claim 1:
Wu teaches:
A learning method to be performed by a computer, the learning method comprising
(Wu, Page 110, Column 1, Paragraph 2, “… we propose an approach named weakly semi-supervised deep learning (WeSed) for multi-label annotation”; Page 114, Column 2, Paragraph 2, “Since we mainly focus on the loss layer, we use the widely used basic architecture in Alexnet as the feature learning model (the CNN components in Fig. 2)”. WeSed is the proposed deep learning method to be performed by a computer utilizing Alexnet).
performing learning of a neural network so as to reduce a value of a first representing a correlation between channels in feature vectors output from at least one of intermediate layers and a final layer in the neural network to which a training data group including a plurality of pieces of training data has been input
(Wu, Page 114, Fig. 2, Paragraph 4, “The overall objective loss we optimize is given as follows (as shown in Fig. 2):
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”. Fig 2. shows a feature learning model using the CNN architecture to perform the reduction of the overall objective loss (Equation 17; where the overall objective loss is interpreted as a value of a first loss ). Equation 17 and Fig. 2 show an overall objective loss (containing multiple loss s) which represent a correlation between channels (interpreted as elements where the loss s utilize semantic similarity and ranking weight to represent a correlation between feature vector elements) in feature vector outputs (f(xi), f(xj), f(xk)). channels are interpreted by the examiner as the output activations the layers. The feature vector outputs are from the final layer (fully connected layer) where the training data is shown in Fig. 2 being a plurality of pieces of training data as input and parsed through the intermediate layers of the CNN architecture).
wherein the feature vectors are represented by channel values of the channels, for each of the plurality of pieces of training data; and
(Wu, Page 114, Fig. 2; Equation 17. Equation 17 is the overall objective loss for the entirety of the feature vectors (f(x)) involved in training via summation. The feature vectors are comprised of the feature vector outputs which are interpreted by the examiner as represented by channel values of the channels (as each feature vector output corresponds to the corresponding feature maps/channels) which is done for each training data image).
Wu teaches correlation between channels but does not explicitly disclose:
the correlation between the channels is a value that is included in the feature vectors and that represents a correlation between groups of the channel values of the training data group, for the channels that are different from each other.
However, Cogswell does not teach:
the correlation between the channels is a value that is included in the feature vectors and that represents a correlation between groups of the channel values of the training data group, for the channels that are different from each other.
(Cogswell, Pages 2-3, Equations 1:
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& 2:
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34
197
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. Equation 1 teaches the covariances between all pairs of activations (channels) within a matrix which correspond to the training data group. Equation 2 also teaches within the training data group which teaches the loss function which utilizes the diagonal function to penalize correlations between channels (which is interpreted by the examiner as for the channels that are different from each other)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the WeSed methodology of Wu for multi-label image annotation, with the use of correlations between different channels. One having ordinary skill in the art would have been motivated to implement this change before the effective filing date of the claimed invention, as this leads to remove redundancy, weighted importance, feature contributions, overfitting reduction, and improving performance (see Cogswell, Abstract, “… we propose a new regularizer called DeCov which leads to significantly reduced overfitting (as indicated by the difference between train and val performance), and better generalization. Our regularizer encourages diverse or non-redundant representations in Deep Neural Networks by minimizing the cross-covariance of hidden activations. This simple intuition has been explored in a number of past works but surprisingly has never been applied as a regularizer in supervised learning. Experiments across a range of datasets and network architectures show that this loss always reduces overfitting while almost always maintaining or increasing generalization performance and often improving performance over Dropout”).
Regarding Claim 2:
Wu/Cogswell teach the method of Claim 1. Wu further teaches:
inputting a supervised training data set and an unsupervised training data set to the neural network as the plurality of pieces of training data,
(Wu, Page 114, Fig. 2. The Training set in Fig. 2 shows the inputting of a supervised training data (Weakly labeled image set Iw) set and an unsupervised training data set (unlabeled image set Iu) to the neural network).
the supervised training data set including a plurality of pieces of supervised training data given annotation information,
(Wu, Page 114, Fig. 2. The Weakly labeled image (interpreted as the supervised training data) set contains given annotation information (where the labels in the experiment are the type of given annotation information within the Multi-Label Image Annotation learning of the WeSed method). For example in Fig 2., where xi = [beach, clouds, sky] (where the dashed boxes notate missing labels) and xj = [beach, clouds, ocean, sky]; thus, the Weakly labeled image is supervised training data not given the annotation information).
the unsupervised training data set including a plurality of pieces of unsupervised training data not given the annotation information;
(Wu, Page 114, Fig. 2. The Unlabeled image (interpreted as the unsupervised training data) set which does not contain any given annotation information (interpreted as labels by the examiner). For example in Fig 2., where xk only notates [sky, person] within the dashed boxes which notates missing labels); thus, the Unlabeled image is unsupervised training data not given the annotation information).
acquiring first feature vectors and second feature vectors,
(Wu, Page 114, Fig. 2. The fully connected layer (output of the CNN) within Fig 2. (f(xi), f(xj), f(xk)) shows the acquiring of the first and second feature vectors).
the first feature vectors being the feature vectors output from the neural network by inputting the supervised training data set,
(Wu, Page 114, Fig. 2. Fig 2: f(xi), f(xj), represents the first feature vectors which are the outputs from the CNN (neural network) by inputting the Weakly labeled image set (supervised training data set)).
the second feature vectors being feature vectors output from the neural network by inputting the unsupervised training data set; and
(Wu, Page 114, Fig. 2. Fig 2: f(xk), represents the second feature vectors which are the outputs from the CNN (neural network) by inputting the Unlabeled labeled image set (unsupervised training data set)).
deriving a value of a second loss and a value of a third loss, the value of the second loss being derived based on the first feature vector and representing a correlation between the annotation information given to the supervised training data set and output information obtained from the neural network by inputting the supervised training data set, the output information corresponding to the annotation information,
(Wu, Page 114, Fig. 2; Page 111, Column 2, Paragraph 3, “… we devise a weakly weighted pairwise ranking (W2PR) loss to optimize the top-k accuracy of multi-label image annotation …:
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”. Fig 2. shows the W2PR loss layer which is derived based on the first feature vectors (f(xi), f(xj)) and represents a correlation between the labels/annotation information of the supervised training data set (weakly labeled image set). Thus, the loss layer utilizes a value of a second loss as it the supervised training set is the input and is derived based on the first feature vectors).
the value of the third loss being a value of the first loss representing a correlation between channels in the second feature vectors,
(Wu, Page 114, Fig. 2; Page 113, Column 1, Paragraph 3, “Here, we expect the learnt features of images in a triplet to meet their relative semantic similarity defined by rsim. Therefore, we optimize the following objective:
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… We call the objective as triplet similarity (TS) loss”. Fig 2. shows the TS loss layer which is a value of the first loss (second half of the Equation 17 (first loss )) and correlates unlabeled and missing labeled images. The TS loss correlates semantic similarity between first and second features; where the second feature vectors are notated as (f(xk)). The loss represents a correlation (rsim/semantic similarity) between the elements in the second feature vectors (f(xk)). Thus, the loss layer utilizes the value of the third loss as it is a derivation of a value of the first loss representing a semantic similarity of the second feature vectors).
wherein at the performing the learning, the learning of the neural network is performed so as to reduce the value of the second loss and the value of the third loss.
(Wu, Page 114, Fig. 2, Paragraph 4, “The overall objective loss we optimize is given as follows (as shown in Fig. 2):
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”. The overall objective loss of the neural network is contains a value of the second loss and the third loss ; thus, the learning method is optimizing the loss to reduce both the values of the second loss and third loss to handle the supervised/unsupervised training data sets).
Regarding Claim 3:
Wu/Cogswell teach the method of Claim 1. Wu further teaches:
inputting a supervised training data set and an unsupervised training data set to the neural network as the plurality of pieces of training data,
(Wu, Page 114, Fig. 2. The Training set in Fig. 2 shows the inputting of a supervised training data (Weakly labeled image set Iw) set and an unsupervised training data set (unlabeled image set Iu) to the neural network).
the supervised training data set including a plurality of pieces of supervised training data given annotation information,
(Wu, Page 114, Fig. 2. The Weakly labeled image (interpreted as the supervised training data) set contains given annotation information (where the labels in the experiment are the type of given annotation information within the Multi-Label Image Annotation learning of the WeSed method). For example in Fig 2., where xi = [beach, clouds, sky] (where the dashed boxes notate missing labels) and xj = [beach, clouds, ocean, sky]; thus, the Weakly labeled image is supervised training data not given the annotation information).
the unsupervised training data set including a plurality of pieces of unsupervised training data not given the annotation information;
(Wu, Page 114, Fig. 2. The Unlabeled image (interpreted as the unsupervised training data) set which does not contain any given annotation information (interpreted as labels by the examiner). For example in Fig 2., where xk only notates [sky, person] within the dashed boxes which notates missing labels); thus, the Unlabeled image is unsupervised training data not given the annotation information).
acquiring first feature vectors that are the feature vectors output from the neural network by inputting the supervised training data set and a second feature vectors that are the feature vectors output from the neural network by inputting the unsupervised training data set; and
(Wu, Page 114, Fig. 2. The fully connected layer (output of the CNN) within Fig 2. (f(xi), f(xj), f(xk)) shows the acquiring of the first and second feature vectors by inputting the supervised training data set (weakly labeled image training set) and unsupervised training data set (unlabeled image training set), respectively).
deriving a value of a second loss , a value of a fourth loss , and a value of a third loss , the value of a second loss being derived based on the first feature vectors and representing a correlation between the annotation information given to the supervised training data set and output information obtained from the neural network by inputting the supervised training data set, the output information corresponding to the annotation information,
(Wu, Page 114, Fig. 2; Page 111, Column 2, Paragraph 3, “… we devise a weakly weighted pairwise ranking (W2PR) loss to optimize the top-k accuracy of multi-label image annotation …:
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”. Fig 2. shows the W2PR loss layer which is derived based on the first feature vectors (f(xi), f(xj)) and represents a correlation between the labels/annotation information of the supervised training data set (weakly labeled image set). Thus, the loss layer utilizes a value of a second loss as it the supervised training set is the input and is derived based on the first feature vectors).
the value of the fourth loss being a value of the first loss representing a correlation between channels in the first feature vectors,
(Wu, Page 114, Fig. 2; Page 113, Column 1, Paragraph 3, “Here, we expect the learnt features of images in a triplet to meet their relative semantic similarity defined by rsim. Therefore, we optimize the following objective:
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… We call the objective as triplet similarity (TS) loss”. Fig 2. shows the TS loss layer which is a value of the first loss (second half of the Equation 17) and correlates unlabeled and missing labeled images (images not given the full annotation information). The loss represents a correlation (rsim/semantic similarity) between the elements in the first feature vectors (f(xi), f(xj)). Thus, the loss layer utilizes the value of the fourth loss which is a value of the first loss representing a semantic similarity of the first feature vectors).
the value of the third loss being a value of the first loss representing a correlation between channels in the second feature vectors,
(Wu, Page 114, Fig. 2; Page 113, Column 1, Paragraph 3, “Here, we expect the learnt features of images in a triplet to meet their relative semantic similarity defined by rsim. Therefore, we optimize the following objective:
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… We call the objective as triplet similarity (TS) loss”. Fig 2. shows the TS loss layer which is a value of the first loss (second half of the Equation 17) and correlates unlabeled and missing labeled images (images not given the full annotation information). The loss represents a correlation (rsim/semantic similarity) between the elements in the second feature vectors (f(xk)). Thus, the loss layer utilizes the value of the third loss which is a value of the first loss representing a semantic similarity of the second feature vectors).
wherein at the performing the learning, the learning of the neural network is performed so as to reduce the value of the second loss , the value of the third loss , and the value of the fourth loss .
(Wu, Page 114, Fig. 2, Paragraph 4, “The overall objective loss we optimize is given as follows (as shown in Fig. 2):
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”. The overall objective loss of the neural network is contains a value of the second loss , the third loss , and the value of the fourth loss ; thus, the learning method is optimizing the loss to reduce the values of the second, third, and fourth loss s to handle the supervised/unsupervised training data sets).
Regarding Claim 4:
Wu/Cogswell teach the method of Claim 1. Wu further teaches:
inputting a supervised training data set to the neural network as the plurality of pieces of training data,
(Wu, Page 114, Fig. 2. The Training set in Fig. 2 shows the inputting of a supervised training data (Weakly labeled image set Iw) set and an unsupervised training data set (unlabeled image set Iu) to the neural network).
the supervised training data set including a plurality of pieces of supervised training data given annotation information;
(Wu, Page 114, Fig. 2. The Weakly labeled image (interpreted as the supervised training data) set contains given annotation information (where the labels in the experiment are the type of given annotation information within the Multi-Label Image Annotation learning of the WeSed method). For example in Fig 2., where xi = [beach, clouds, sky] (where the dashed boxes notate missing labels) and xj = [beach, clouds, ocean, sky]; thus, the Weakly labeled image is supervised training data not given the annotation information).
acquiring first feature vectors that are the feature vectors output from the neural network by inputting the supervised training data set; and
(Wu, Page 114, Fig. 2. The fully connected layer (output of the CNN) within Fig 2. (f(xi), f(xj)) shows the acquiring of the first feature vectors by inputting the supervised training data set (weakly labeled image training set)).
deriving a value of a second loss and a value of a fourth loss , the value of the second loss being derived based on the first feature vectors and representing a correlation between the annotation information given to the supervised training data set and output information obtained from the neural network by inputting the supervised training data set, the output information corresponding to the annotation information,
(Wu, Page 114, Fig. 2; Page 111, Column 2, Paragraph 3, “… we devise a weakly weighted pairwise ranking (W2PR) loss to optimize the top-k accuracy of multi-label image annotation …:
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”. Fig 2. shows the W2PR loss layer which is derived based on the first feature vectors (f(xi), f(xj)) and represents a correlation between the labels/annotation information of the supervised training data set (weakly labeled image set). Thus, the loss layer utilizes a value of a second loss as it the supervised training set is the input and is derived based on the first feature vectors).
the value of the fourth loss being a value of the first loss representing a correlation between channels in the first feature vectors,
(Wu, Page 114, Fig. 2; Page 113, Column 1, Paragraph 3, “Here, we expect the learnt features of images in a triplet to meet their relative semantic similarity defined by rsim. Therefore, we optimize the following objective:
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… We call the objective as triplet similarity (TS) loss”. Fig 2. shows the TS loss layer which is a value of the first loss (second half of the Equation 17) and correlates unlabeled and missing labeled images (images not given the full annotation information). The loss represents a correlation (rsim/semantic similarity) between the elements in the first feature vectors (f(xi), f(xj)). Thus, the loss layer utilizes the value of the fourth loss which is a value of the first loss representing a semantic similarity of the first feature vectors).
wherein at the performing the learning, the learning of the neural network is performed so as to reduce the value of second loss and the value of fourth loss .
(Wu, Page 114, Fig. 2, Paragraph 4, “The overall objective loss we optimize is given as follows (as shown in Fig. 2):
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”. The overall objective loss of the neural network is contains a value of second loss , and the value of the fourth loss ; thus, the learning method is optimizing the loss to reduce the values of the second, third, and fourth loss s to handle the supervised/unsupervised training data sets).
Regarding Claim 5:
Wu/Cogswell teach the method of Claim 1. Wu further teaches:
wherein a correlation coefficient is used for calculation of the value of the first loss .
(Wu, Page 113, Column 1, Paragraph 2. “Here, we expect the learnt features of images in a triplet to meet their relative semantic similarity defined by rsim. Therefore, we optimize the following objective:
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”. rsim is a correlation coefficient that is used for calculation of the value of the first loss as TS loss utilizes semantic similarity via rsim where the semantic similarity is a correlation coefficient).
Regarding Claim 6:
Wu/Cogswell teach the method of Claim 1. Wu further teaches:
wherein the plurality of pieces of training data includes a plurality of groups each including a plurality of supervised training data sets and a plurality of groups each including a plurality of unsupervised training data sets.
(Wu, Page 114, Fig. 2; Page 115, Table 1. Fig. 2. shows the method handling the training data sets which include a plurality of groups including supervised and unsupervised training data set which are shown in detail within Table 1).
Regarding Claim 7:
Wu/Cogswell teach the method of Claim 1. Wu further teaches:
receiving an input of a learning condition including at least one of a network structure of the neural network as a target of the learning, the training data to be used in the learning, and a description of setting to be used at a time of the learning,
(Wu, Page 114, Fig. 2, Column 2, Paragraph 2, “In our network, an image triplet is first sampled from the training image set. Before feeding the images in triplets to the CNN, each image is resized into 256 256 … we mainly focus on the loss layer, we use the widely used basic architecture in Alexnet [18] as the feature learning model (the CNN components in Fig. 2) … the learnt features are fed into the triplet similarity loss layer which computes the gradient of Equation (11). If the label information is available, the learnt feature is fed into a ranking layer as the activation, and the output of the ranking layer is fed into the W2PR loss layer which computes the gradient of Equation (3). The optimization of the entire network is achieved with stochastic gradient method … parameter settings from the existing literature, the momentum is set to 0.9, and the batch size is set to 50. The learning rate for our model is set to 0.00002 at the start and we drop the learning rate after several epochs by a factor of 10”; Page 119, Column 1, Paragraph 2, “… we attempt to harness such images, i.e., the weakly labelled images and the unlabeled images, in a deep learning manner. To that end, we propose an approach named weakly semi-supervised deep learning (WeSed) for multi-label annotation. In WeSed, we devise a pairwiseranking loss and a triplet-ranking loss to fine-tune the convolutional neural network with weakly labeled and unlabeled images. The pairwise-ranking loss is employed to handle the weakly labeled images and the triplet-ranking loss is conducted to address the problem that images are possibly most unlabeled”. The WeSed approach is a that utilizes a Convolutional Neural Network architecture (structure) to perform the learning methodology for multi-label annotation (interpreted as the learning condition as the model was conditioned with the configurations/settings to optimize multi-label annotations for semi-supervised learning). The model receives an input of a learning condition (the training datasets with missing or unlabeled annotations). The learning rate is a description of a setting to be used at a time and WeSed drops the learning rate after a certain amount of time).
wherein at the performing the learning, the learning of the neural network is performed in accordance with the learning condition having been received.
(Wu, Page 114, Fig. 2; Tables 4-10. Tables 4-10 denote the performances of the model performing the learning (training) with different datasets; where the WeSed approach utilizing the CNN denoted in Fig. 2 is curated to perform multi-label image annotations).
Regarding Claims 9-13:
Claims 9-13 incorporate substantively all the limitations of Claims 1-4 and 7 in a computer program product and further recites comprising a computer-readable medium including programmed instructions, the instructions causing a computer to execute (Page 114, Column 2, Paragraph 2, “Since we mainly focus on the loss layer, we use the widely used basic architecture in Alexnet as the feature learning model (the CNN components in Fig. 2)”. The WeSed methodology is handled on Alexnet which is a software package developed for CNN architectures for image classification tasks; which implies the process is done on a computing device, in which a CRM is inherent); thus, Claims 9-13 are rejected for reasons set forth in the rejections of Claims 1-4 and 7, respectively.
Regarding Claims 15-19:
Claims 15-19 incorporate substantively all the limitations of Claims 1-4 and 7 in a learning apparatus and further recites comprising one or more hardware processors configured to (Page 114, Column 2, Paragraph 2, “Since we mainly focus on the loss layer, we use the widely used basic architecture in Alexnet as the feature learning model (the CNN components in Fig. 2)”. The WeSed methodology is handled on Alexnet which is a software package developed for CNN architectures for image classification tasks; which implies the process is done on a computing device, in which a processor is inherent); thus, Claims 15-19 are rejected for reasons set forth in the rejections of Claims 1-4 and 7, respectively.
Claims 8, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al., “Weakly Semi-Supervised Deep Learning for Multi-Label Image Annotation” in view of Cogswell et al., “Reducing Overfitting in Deep Networks by Decorrelating representations” in view of Park et al., “HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks”.
Regarding Claim 8:
Wu/Cogswell teach the method of Claim 7. However, Wu does not explicitly disclose:
displaying a display screen including at least one of a learning progress state of the neural network and a content of change recommendation for the learning condition depending on the learning progress state.
However, Park explicitly teaches:
displaying a display screen including at least one of a learning progress state of the neural network and a content of change recommendation for the learning condition depending on the learning progress state.
(Park, Page 1, Fig. 1; Page 2, Column 1, Paragraph 2, “To this end, we propose HyperTendril (Fig. 1), a web-based visual analytics system that supports HyperOpt tasks, where users can effectively perform HyperOpt through an iterative and interactive tuning procedure, allowing them to fine-tune the optimal hyperparameters based on their domain knowledge and insights obtained from the previous results. In detail, HyperTendril helps the users progressively refine their search spaces by explicitly highlighting relevant hyperparameters and the promising ranges to explore further, based on a quantitative analysis on the objective model performance (e.g., a test accuracy) (Fig. 1(C))”. The interactive display screen of HyperTendril offers statistics and settings/configurations/parameters/etc. (content of change) depending on the progress state of the training (as the display updates iteratively) for changes. Thus, the display screen includes a learning progress state (interpreted as training by the examiner) of the neural network architecture and a content of change recommendation (by supplying relevant information for the user to decide on changes)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the WeSed methodology of Wu/Cogswell for multi-label image annotation, with the use of the Park’s interactive display screen to review and recommend changes based off the learning state. One having ordinary skill in the art would have been motivated to implement this change before the effective filing date of the claimed invention, as this leads to more informatics for the user, alleviates tedious processes, visual representations, and the use of recommendations (see Park, Page 2, Column 1, Paragraph 2, “In order to alleviate the pain of tedious processes, human intuition for AutoML results, such as the behavior of search algorithms, the effect of optimization algorithm setting, and the characteristics of hyperparameters, should be accompanied. Thus, effective and efficient human intervention is critical during the HyperOpt process, which necessitates a visual analytics system that can leverage human insights to steer the optimization process in a user-driven manner”).
Regarding Claim 14:
Claim 14 incorporates substantively all the limitations of Claim 8 in a computer program product (thus, a manufacture) and further recites comprising a computer-readable medium including programmed instructions, the instructions causing a computer to execute (Page 114, Column 2, Paragraph 2, “Since we mainly focus on the loss layer, we use the widely used basic architecture in Alexnet as the feature learning model (the CNN components in Fig. 2)”. The WeSed methodology is handled on Alexnet which is a software package developed for CNN architectures for image classification tasks; which implies the process is done on a computing device, in which a CRM is inherent); thus, Claim 14 is rejected for reasons set forth in the rejection of Claim 8.
Regarding Claim 20:
Claim 20 incorporate substantively all the limitations of Claim 8 in a learning apparatus and further recites comprising one or more hardware processors configured to (Page 114, Column 2, Paragraph 2, “Since we mainly focus on the loss layer, we use the widely used basic architecture in Alexnet as the feature learning model (the CNN components in Fig. 2)”. The WeSed methodology is handled on Alexnet which is a software package developed for CNN architectures for image classification tasks; which implies the process is done on a computing device, in which a processor is inherent); thus, Claim 20 is rejected for reasons set forth in the rejections of Claim 8.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 IBRAHIM RAHMAN whose telephone number is (703)756-1646. The examiner can normally be reached M-F 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/I.R./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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