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 .
DETAILED ACTION
Claims 6-11 are presented for examination in this application (17918173) filed 10/11/2022, having an effective filing date of 04/23/2020 with priority to PCT JP2020/017502. The Examiner cites particular sections in the references as applied to the claims below for the convenience of the applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant(s) fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner
Response to Arguments
Applicant’s arguments and remarks filed 10/07/2025 have been fully considered. The arguments and remarks regarding the 35 U.S.C 101 rejections were not found to be persuasive. The arguments and remarks regarding the 35 U.S.C 103 rejection was considered to be persuasive. The 35 U.S.C 101 rejection has been maintained, however, the 35 U.S.C 103 rejections have been overcome.
35 U.S.C 101
Applicant asserts:
Applicant asserts “In one aspect, the claimed technique first updates the class representative vectors to satisfy the relationship of the inter-class distance error Ld< d. For example, the optimization unit 107 updates the class representative vectors to optimize the objective function of L = Ld - d using a gradient method. Here, d is a predetermined integer. Next, the optimization unit 107 optimizes the objective function L = Lo using the gradient method with the class representative vectors fixed. That is, after a position of the class representative vector of each class on the feature space is determined, the classification error is optimized using the gradient method, and thereby the parameters used by the feature extraction unit 101 are optimized. See, paragraph [0051]. Due to the above processing, the parameters used by the feature extraction unit 101 are optimized such that the distances between multiple classes serving as classification destinations in the feature space are uniform. Furthermore, the feature value extracted by the feature extraction unit 101 is mapped to any of areas of the multiple classes in the feature space. In this way, the classification accuracy by the computer is improved. Therefore, the pending claims cover a particular solution to achieve the desired outcome. See, MPEP 2106.05(a)”
Examiner asserts:
Examiner respectfully disagrees. Examiner asserts that the claim elements identified and emphasized by applicant are directed to the abstract mental and abstract mathematical categories. Any elements which are not directed to mental processes are recited in a highly generic manner and do not impose any meaningful limits on the judicial exception. Applicant has not provided any reasoning as to why any particular claim elements would amount to anything more than mere instructions to apply a judicial exception, adding insignificant extra-solution activity to the judicial exception, and generally linking the use of a judicial exception to a particular technological environment/field of use.
35 U.S.C 103
Applicant asserts:
Applicant asserts “Of note, claim 6 has been amended to recite
"the processor determines a position of the class representative vector of every class in the feature space by updating the class representative vector of every class using a gradient method to optimize an objective function L=Ld-d and then optimizes the parameter by optimizing the classification error using a gradient method, the Ld being the distance error and the d being a predetermined integer" … “As conceded by the examiner, Marques does not disclose the claimed configuration. The examiner appears to rely on Munkhdalai to teach this aspect. Munkhdalai, however, describes a configuration that optimizes model parameters using stochastic gradient descent (SGD). Munkhdalai does not determine a position of the class representative vector of every class in the feature space by updating the class representative vector of every class using a gradient method to optimize an objective function L=La-d and then optimize the parameter by optimizing the classification error using a gradient method (the La is the distance error and the d is a predetermined integer). Mahrooghy also fails to cure this deficiency. Therefore, it is respectfully submitted that amended claim 6 defines patentable subject matter over this combination of references.
Claims 7 and 10 recite similar limitations and thus should be allowable, along with claims depending therefrom, for the same reasons as Claim 6. With regard to claims 8 and 9, these claims differ from Marques as follows: "in the optimizing the parameter by optimizing an objective function L=Lc + ALd using a gradient method, the L being the classification error, the Ld being the distance error, and the A being a Lagrange coefficient.". The above configuration enables the achievement of the "Thus, optimization can be achieved such that the distances between the classes are maximized, that is, the cosine similarity is reduced. As a result, the classification accuracy can be improved". See paragraph [0073]. Marques does not teach or suggest this claimed aspect. The examiner again relies on Munkhdalai to teach this claimed aspect. Munkhdalai, however, describes a configuration that optimizes model parameters using stochastic gradient descent (SGD). Munkhdalai does not optimize the parameter by optimizing an objective function L=Lo+ALa using a gradient method (the Lc is the classification error, the La is the distance error and the A is a Lagrange coefficient). Because the claimed configuration is not described in either Munkhdalai and Mahrooghy, it is respectfully submitted that claims 8 and 9 also defines patentable subject matter over this combination of references. Accordingly, Applicant respectfully requests reconsideration and withdrawal of this rejection. ”.
Examiner asserts:
The Examiner respectfully agrees.
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 6-11 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
Regarding claim 6 (currently amended):
Step 1 – Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a machine.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas:
extract a feature vector using input data — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
optimize a parameter used in the feature extractor based on a classification error obtained using a correct answer data and the classification result and a distance error between the class representative vectors such that areas of features of the classes in a feature space do not overlap each other — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
wherein the processor determines a position of the class representative vector of every class in the feature space by updating the class representative vector of every class using a gradient method to optimize an objective function L =Ld-d and then optimizes the parameter by optimizing the classification error using a gradient method, the Ld being the distance error and the d being a predetermined integer — this limitation is directed to mathematical calculations 2106.04(a)(2).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
a parameter optimization apparatus — this limitation is directed to the field of use (see MPEP 2106.05(h)) as it merely limits the field of the apparatus to parameter optimization.
a processor — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
a storage medium having computer program instructions stored thereon, when executed by the processor — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
acquire a classification result of the feature vector and a class representative vector of every class serving as a classification target — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQd at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “configured to acquire a classification result of the feature vector and a class representative vector of every class serving as a classification target ” limitation was found to be an insignificant extra solution activity in claim 6. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply the exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Thus, the claim is not patent eligible. MPEP 2106.05(f) cannot integrate the abstract idea into a practical application.
Regarding claim 7 (currently amended):
Step 1 – Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas:
extracting a feature vector using input data — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
optimizing a parameter used in the extracting based on a classification error obtained using correct answer data and the classification result and a distance error between the class representative vectors— this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
determining a position of the class representative vector of every class in the feature space by updating the class representative vector of every class using a gradient method to optimize an objective function L=Ld-d and then optimizing the parameter by optimizing the classification error using a gradient method, the Ld being the distance error and the d being a predetermined integer — this limitation is directed to mathematical calculations 2106.04(a)(2).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
a parameter optimization method — this limitation is directed to the field of use (see MPEP 2106.05(h)) as it merely limits the field of method to parameter optimization.
acquiring a classification result of the feature vector and a class representative vector of every class serving as a classification target — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQd at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “acquiring a classification result of the feature vector and a class representative vector of every class serving as a classification target” limitation was found to be an insignificant extra solution activity in claim 7. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply the exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Thus, the claim is not patent eligible. MPEP 2106.05(f) cannot integrate the abstract idea into a practical application.
Regarding claim 8 (currently amended):
Step 1 – Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas:
extracting a feature vector using input data — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
optimizing a parameter used in the extracting based on a classification error obtained using correct answer data and the classification result and a distance error between the class representative vectors— this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
in the optimizing, the parameter by optimizing an objective function L=Lc + λLd using a gradient method, the Lc being the classification error, the Ld being the distance error, and the λ being a Lagrange coefficient — this limitation is directed to mathematical calculations 2106.04(a)(2).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
a parameter optimization method — this limitation is directed to the field of use (see MPEP 2106.05(h)) as it merely limits the field of method to parameter optimization.
acquiring a classification result of the feature vector and a class representative vector of every class serving as a classification target — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQd at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “acquiring a classification result of the feature vector and a class representative vector of every class serving as a classification target” limitation was found to be an insignificant extra solution activity in claim 8. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply the exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Thus, the claim is not patent eligible. MPEP 2106.05(f) cannot integrate the abstract idea into a practical application.
Regarding claim 9:
Step 1 – Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to an apparatus.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas:
extract a feature vector using input data — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
optimize a parameter used based on a classification error obtained using correct answer data and the classification result and a distance error between the class representative vectors such that areas of the features of the classes in a feature space do not overlap each other — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
wherein the processor optimizes the parameter by optimizing an objective function L=Lc + λLd using a gradient method, the Lc being the classification error, the Ld being the distance error, and the λ being a Lagrange coefficient — this limitation is directed to mathematical calculations 2106.04(a)(2).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
a parameter optimization apparatus — this limitation is directed to the field of use (see MPEP 2106.05(h)) as it merely limits the field of method to parameter optimization.
a processor configured to — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
acquire a classification result of the feature vector and a class representative vector of every class serving as a classification target — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQd at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “acquire a classification result of the feature vector and a class representative vector of every class serving as a classification target” limitation was found to be an insignificant extra solution activity in claim 9. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply the exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Thus, the claim is not patent eligible. MPEP 2106.05(f) cannot integrate the abstract idea into a practical application.
Regarding claim 10:
Step 1 – Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to non-transitory recording medium.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas that are present in the parent claim of claim 7.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
a non-transitory recording medium configured to record a computer program for causing a computer to execute the parameter optimization method according to claim 7 — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
Regarding claim 11:
Step 1 – Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to non-transitory recording medium.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas that are present in the parent claim of claim 8.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
a non-transitory recording medium configured to record a computer program for causing a computer to execute the parameter optimization method according to claim 8 — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
“Cross Attention Network for Few-shot Classification” — Hou et al. — discloses classification and feature extraction as well as working with non-overlapping of classes
US20220188700A1 — discloses optimizing hyperparameters and improving classification performance
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.
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/ANDREW BRACERO/Examiner, Art Unit 2126
/VAN C MANG/Primary Examiner, Art Unit 2126