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
This Office Action is sent in response to Applicant’s Communication received
on 07/11/2025 for application number 17/577029.
Response to Amendments
Claims 1-3 and 5 have been amended.
Claim 4 has been canceled.
Claims 1-3 and 5 remain pending in the application.
The amendment filed 07/11/2025 is sufficient to overcome the 112(b) rejections to claims 1-3 and 5. The previous rejections have been withdrawn.
Argument 1, regarding the 112 rejections, applicant argues that the 112 rejections should be withdrawn after amending the claims to clarify that “the pre-trained neural networks” refer to each of the corporate classification neural network, the patent classification neural network, the corporate evaluation neural network, and the patent evaluation neural network. Examiner agrees and the 112(b) rejections have been withdrawn.
Argument 2, regarding the 101 rejections, applicant argues that amended limitations such as “generating a corporate classification value by inputting the assessment corporate information and assessment patent information into a pre-trained corporate classification neural network of an embedded computer; generating a patent classification value by inputting the assessment corporate information and assessment patent information into a pre-trained patent classification neural network of the embedded computer; generating a corporate assessment index by inputting the corporate classification value and a first comparison signal pre-stored in a database into a pre-trained corporate evaluation neural network; generating a patent assessment index by inputting the patent classification value and a second comparison signal pre-stored in the database into a pre-trained patent evaluation neural network” do not recite mere data gathering.
Examiner notes that these limitations are interpreted as mathematical concepts in view of lines 13-30 of page 10 and lines 1-16 of page 12 of the specification of the instant application, which explain outputting a unique corporate classification value for the assessment target corporate includes calculating a corporate assessment index with the use of mathematical concepts such as a ReLU function, sigmoid function, tanh function, one-hot encoding, MSE, and CEE. In view of lines 31-33 of page 10, lines 1-14 of page 11, and lines 1-16 of page 12 of the specification of the instant application, outputting a unique patent classification value includes calculating a patent assessment index with the use of mathematical concepts such as a ReLU function, sigmoid function, tanh function, one-hot encoding, MSE, and CEE.
Applicant also argues that iteratively updating neural networks with user feedback is integrating the judicial exception into a practical application. Examiner respectfully disagrees because collecting user feedback is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g), and updating neural networks is generally linking the abstract idea to the technological environment of machine learning, as discussed in MPEP 2106.05(h).
Applicant also argues that these limitations reflect a tangible improvement in patent assessment accuracy. Examiner notes that there is nothing pertaining to “patent assessment accuracy” in the claims and it is not clear what this improvement is based on the claim language. To evaluate an improvement to a computer or technical field, the specification must set for than improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1) and 2106.05(a).
The full 101 rejections are outlined below.
Argument 2, regarding the 103 rejections, applicant argues that Beers in view of Sun does not teach the amended features of claim 1.
Examiner respectfully disagrees because Beers teaches generating a corporate classification value by inputting the assessment corporate information and assessment patent information into a pre-trained corporate classification neural network of an embedded computer (“A significant advantage of the use of machine learning when identifying input factors and computing the classification model is that the model can be continuously updated in response to changes in the market”, C5:L24-27. “The input sets comprise a random or pseudo-random sampling of issued patents from a given patent office. In a preferred embodiment, the system then creates multiple binary classifiers, each predicting the maintenance of patent for a given maintenance period.”, C5:L18-22. The output of each of the binary classifiers is averaged out into a final score for the features predicting the maintenance of the patent, C5:L21-24.); generating a patent classification value by inputting the assessment corporate information and assessment patent information into a pre-trained patent classification neural network of the embedded computer (One of the input features for the classifiers shown in table 1 include number of claims, C4:L45-68, C5:L1-8. The output of each of the binary classifiers is averaged out into a final score for the features predicting the maintenance of the patent, C5:L21-24. Binary classifiers made with a list of features associated with a weighted scale or standardized score are compared with a cost function, such as an area under a curve (AUC). The features are derived from a patent database. The result of each iteration of the neural network is compared with the highest yielding binary classifier set thus far. A genetic algorithm may be used to improve upon the candidate set of binary classifiers, and iterations of the genetic algorithm are continued to maximize the area under the curve of the ROC. C23:L57-68, C24:L1-12); generating a corporate assessment index by inputting the corporate classification value and a first comparison signal pre-stored in a database into a pre-trained corporate evaluation neural network (Iterative learning may be used with corporate disclosure information to calculate predictions or decisions based on input data and compares it with the crawled corporate disclosure information during iterative learning, page 4, paragraphs 4-5); generating a patent assessment index by inputting the patent classification value and a second comparison signal pre-stored in the database into a pre-trained patent evaluation neural network (Binary classifiers made with a list of features associated with a weighted scale or standardized score are compared with a cost function, such as an area under a curve (AUC). The features are derived from a patent database. The result of each iteration of the neural network is compared with the highest yielding binary classifier set thus far. A genetic algorithm may be used to improve upon the candidate set of binary classifiers, and iterations of the genetic algorithm are continued to maximize the area under the curve of the ROC. C23:L57-68, C24:L1-12); transmitting patent assessment information to the user terminal based on the corporate assessment index and the patent assessment index (“a user interface receiving the target patent information for the target patent; an estimate requester requesting from the machine-learning based artificial intelligence device, the estimate of patent quality for the target patent; and a user interface providing, to a user, information indicating the estimate of patent quality”, claim 8), wherein the embedded computer performs iterative machine learning training using user- provided feedback to continuously update weights of the pre-trained neural networks (A genetic algorithm may be used to improve upon the candidate set of binary classifiers, and iterations of the genetic algorithm are continued to maximize the area under the curve of the ROC. C23:L57-68, C24:L1-12. Learning includes parameters set by a user, C3:L68, C4:L1-3).
The full prior art rejections are outlined below.
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 1-3 and 5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claims recite a method, which is one of the four categories of eligible subject matter.
Claim 1
Step 2A Prong 1: The claims recite the following limitations:
generating a corporate classification value by inputting the assessment corporate information and assessment patent information into a pre-trained corporate classification neural network of an embedded computer (Mathematical Concept); generating a patent classification value by inputting the assessment corporate information and assessment patent information into a pre-trained patent classification neural network of the embedded computer (Mathematical Concept); generating a corporate assessment index by inputting the corporate classification value and a first comparison signal pre-stored in a database into a pre-trained corporate evaluation neural network (Mathematical Concept); generating a patent assessment index by inputting the patent classification value and a second comparison signal pre-stored in the database into a pre-trained patent evaluation neural network (Mathematical Concept).
In view of lines 13-30 of page 10 and lines 1-16 of page 12 of the specification of the instant application, outputting a unique corporate classification value for the assessment target corporate includes calculating a corporate assessment index with the use of mathematical concepts such as a ReLU function, sigmoid function, tanh function, one-hot encoding, MSE, and CEE. In view of lines 31-33 of page 10, lines 1-14 of page 11, and lines 1-16 of page 12 of the specification of the instant application, outputting a unique patent classification value includes calculating a patent assessment index with the use of mathematical concepts such as a ReLU function, sigmoid function, tanh function, one-hot encoding, MSE, and CEE. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not incorporated into practical
application. The claims recite the following additional elements:
obtaining assessment patent information of an assessment target patent and assessment corporate information of an assessment target corporate possessing the assessment target patent from a user terminal; generating an input signal based on the assessment corporate information and the assessment patent information; … and transmitting a patent assessment information to the user terminal based on the corporate assessment index and the patent assessment index, wherein the embedded computer performs iterative machine learning training using user- provided feedback to continuously update weights of the pre-trained neural networks.
Obtaining user input and transmitting outputs to the user is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). Inputting data and signals to a neural network and iteratively updating neural networks are generally linking the abstract idea to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claim is directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. Obtaining user input and transmitting outputs to the user is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). Inputting data and signals to a neural network and iteratively updating neural networks are generally linking the abstract idea to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claim is not patent eligible.
Dependent Claims
Claim 2
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. The claims recite the following limitations:
transmitting the patent assessment information includes generating patent assessment information based on the corporate assessment index and the patent assessment index (Mathematical Concept).
In view of lines 1-14 and 30-34 of page 11 and lines 1-16 of page 12 of the specification of the instant application, generating patent assessment information using the neural network includes calculating patent assessment indices, with the use of mathematical concepts such as a ReLU function, sigmoid function, tanh function, one-hot encoding, MSE, and CEE. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not incorporated into practical
application. The claims recite the following additional elements:
wherein generating the input signal includes generating a first input signal based on the assessment corporate information, and generating a second input signal based on the assessment patent information.
Inputting signals and data to a neural network is generally linking the abstract idea to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claim is directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. Inputting signals and data to a neural network is generally linking the abstract idea to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claim is not patent eligible.
Claim 3
Step 2A Prong 1: The judicial exceptions of claim 2 are incorporated. The claims recite the following limitations:
wherein the corporate classification neural network outputs a unique corporate classification value for the assessment target corporate based on the input (Mathematical Concept);… wherein the patent classification neural network outputs a unique patent classification value for the assessment target patent based on the input (Mathematical Concept).
In view of lines 13-30 of page 10 and lines 1-16 of page 12 of the specification of the instant application, outputting a unique corporate classification value for the assessment target corporate includes calculating a corporate assessment index with the use of mathematical concepts such as a ReLU function, sigmoid function, tanh function, one-hot encoding, MSE, and CEE. In view of lines 31-33 of page 10, lines 1-14 of page 11, and lines 1-16 of page 12 of the specification of the instant application, outputting a unique patent classification value includes calculating a patent assessment index with the use of mathematical concepts such as a ReLU function, sigmoid function, tanh function, one-hot encoding, MSE, and CEE. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not incorporated into practical
application. The claims recite the following additional elements:
wherein the corporate classification neural network takes as an input at least one of industry information, financial information, and stock price information of the assessment target corporate, at least one of a classification code, a number of forward cited documents, a number of backward cited documents, and a number of claims of the assessment target patent.
Inputting signals and data to a neural network is generally linking the abstract idea to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claim is directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. Inputting signals and data to a neural network is generally linking the abstract idea to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claim is not patent eligible.
Claim 5
Step 2A Prong 1: The judicial exceptions of claim 3 are incorporated. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not incorporated into practical
application. The claims recite the following additional elements:
wherein the patent classification neural network includes as an input a third input signal embedding contents described in one or more items of a patent specification including the claims of the assessment target patent.
Inputting signals and data to a neural network is generally linking the abstract idea to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claim is directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. Inputting signals and data to a neural network is generally linking the abstract idea to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Beers et al (Pub. No.: US 12014250 B2), hereafter Beers in view of Ahn Sang Sun et al (Pub. No.: KR 20200069124 A), hereafter Sun.
Regarding claim 1, Beers teaches obtaining assessment patent information of an assessment target patent…from a user terminal (“The device may also include a user information manager configured to receive patent information for a target patent and to report the estimate of patent quality according to the final set of binary classifier” , C2:L45-48); generating an input signal based on…the assessment patent information (Patent information is used as input for a neural network, C3:L43-54); generating a corporate classification value by inputting the assessment corporate information and assessment patent information into a pre-trained corporate classification neural network of an embedded computer (“A significant advantage of the use of machine learning when identifying input factors and computing the classification model is that the model can be continuously updated in response to changes in the market”, C5:L24-27. “The input sets comprise a random or pseudo-random sampling of issued patents from a given patent office. In a preferred embodiment, the system then creates multiple binary classifiers, each predicting the maintenance of patent for a given maintenance period.”, C5:L18-22. The output of each of the binary classifiers is averaged out into a final score for the features predicting the maintenance of the patent, C5:L21-24.); generating a patent classification value by inputting the assessment corporate information and assessment patent information into a pre-trained patent classification neural network of the embedded computer (One of the input features for the classifiers shown in table 1 include number of claims, C4:L45-68, C5:L1-8. The output of each of the binary classifiers is averaged out into a final score for the features predicting the maintenance of the patent, C5:L21-24. Binary classifiers made with a list of features associated with a weighted scale or standardized score are compared with a cost function, such as an area under a curve (AUC). The features are derived from a patent database. The result of each iteration of the neural network is compared with the highest yielding binary classifier set thus far. A genetic algorithm may be used to improve upon the candidate set of binary classifiers, and iterations of the genetic algorithm are continued to maximize the area under the curve of the ROC. C23:L57-68, C24:L1-12); generating a corporate assessment index by inputting the corporate classification value and a first comparison signal pre-stored in a database into a pre-trained corporate evaluation neural network (Iterative learning may be used with corporate disclosure information to calculate predictions or decisions based on input data and compares it with the crawled corporate disclosure information during iterative learning, page 4, paragraphs 4-5); generating a patent assessment index by inputting the patent classification value and a second comparison signal pre-stored in the database into a pre-trained patent evaluation neural network (Binary classifiers made with a list of features associated with a weighted scale or standardized score are compared with a cost function, such as an area under a curve (AUC). The features are derived from a patent database. The result of each iteration of the neural network is compared with the highest yielding binary classifier set thus far. A genetic algorithm may be used to improve upon the candidate set of binary classifiers, and iterations of the genetic algorithm are continued to maximize the area under the curve of the ROC. C23:L57-68, C24:L1-12); transmitting patent assessment information to the user terminal based on the corporate assessment index and the patent assessment index (“a user interface receiving the target patent information for the target patent; an estimate requester requesting from the machine-learning based artificial intelligence device, the estimate of patent quality for the target patent; and a user interface providing, to a user, information indicating the estimate of patent quality”, claim 8), wherein the embedded computer performs iterative machine learning training using user- provided feedback to continuously update weights of the pre-trained neural networks (A genetic algorithm may be used to improve upon the candidate set of binary classifiers, and iterations of the genetic algorithm are continued to maximize the area under the curve of the ROC. C23:L57-68, C24:L1-12. Learning includes parameters set by a user, C3:L68, C4:L1-3).
Beers doesn’t appear to explicitly teach obtaining…assessment corporate information of an assessment target corporate…generating an input signal based on the assessment corporate information.
Sun teaches obtaining…assessment corporate information of an assessment target corporate (“The patent information confirmation unit 100 searches for corporate disclosure information on the Financial Supervisory Service site and KIPRIS patent information on the JPO site to confirm the authenticity of the target patent information.”, page 3, paragraph 7)…generating an input signal based on the assessment corporate information (artificial intelligence robot of the cross-checking module 120 may take corporate information as input, page 4, paragraphs 4-5).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Beers and Sun before them, to include Sun’s specific teaching of collecting corporate information of a target patent and inputting it in an artificial intelligence robot in Beers’ system of Machine Learning-based Patent Quality Metric. One would have
been motivated to make such a combination of collecting corporate information of a target patent and inputting it in an artificial intelligence robot (see Sun page 3, paragraph 7 and page 4, paragraphs 4-5) and building a classification model that can be continuously updated in response to changes in the market to maximize prediction accuracy (see Beers C5:L24-30).
Regarding claim 2, Beers in view of Sun teaches the elements of claim 1 as outlined above. Beers further teaches wherein: generating the input signal includes …, and generating a second input signal based on the assessment patent information (Patent information is used as input for a neural network, C3:L43-54) and transmitting the patent assessment information includes generating patent assessment information based the corporate assessment index and the patent assessment index (Classifiers are trained using a training set which includes corporate information such as described in table 1 on column 4. “the system then creates multiple binary classifiers, each predicting the maintenance of patent for a given maintenance period. The final output of each classifier is combined into a final score”, C5:L17-23).
Sun further teaches generating a first input signal based on the assessment corporate information (AI may take corporate information as input, page 4, paragraphs 4-5).
Regarding claim 3, Beers in view of Sun teaches the elements of claim 2 as outlined above. Sun further teaches wherein the corporate classification neural network takes as an input at least one of industry information, financial information, and stock price information of the assessment target corporate.
Beers further teaches at least one of a classification code, a number of forward cited documents, a number of backward cited documents, and a number of claims of the assessment target patent, and wherein the corporate classification neural network outputs a unique corporate classification value for the assessment target corporate based on the input;… and a second input signal encoding the assessment patent information including at least one of the classification code, the number of forward cited documents, the number of backward cited documents, and the number of claims of the assessment target patent, and wherein the the patent classification neural network outputs a unique patent classification value for the assessment target patent based on the input (One of the input features for the classifiers shown in table 1 include number of claims, C4:L45-68, C5:L1-8. The output of each of the binary classifiers is averaged out into a final score for the features predicting the maintenance of the patent, C5:L21-24).
Regarding claim 5, Beers in view of Sun teaches the elements of claim 3 as outlined above. Beers further teaches wherein the patent classification neural network includes as an input a third input signal embedding contents described in one or more items of a patent specification including the claims of the assessment target patent (Input data for the classification model includes the list parts 1-6, which includes any corrections made to the patent specification, C17:L1-2, C18:L6-11, C20:L45-50).
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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/I.M./Examiner, Art Unit 2141
/KIEU D VU/Supervisory Patent Examiner, Art Unit 2141