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 .
Allowable Subject Matter
Claims 16-18 objected to as being dependent upon a rejected base claim, but would be allowable over the prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/18/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1 analysis:
Independent Claim 1 recites, in part, a system comprised of a processor and memory therefore falling into the statutory category of machine. Independent Claim 8 recites, in part, a computer implemented method therefore falling into the statutory category of process. Independent Claim 15 recites, in part, a computer-program product therefore falling into the statutory category of manufacture.
Regarding Claim 1:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“extract one or more feature groups associated with the user from unstructured user data associated with the user”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses extracting unstructured data from a dataset.
“generate a user input vector using the unstructured user data, the user input vector representing the one or more feature groups associated with the user”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses taking unformatted data and putting it into a vector format.
“generate an ensembled output predicting the behavior of the user with respect to the obligation”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses creating an aggregated output in order to predict the behavior of a user.
Accordingly, at Step 2A: prong one, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“A system, comprising: one or more processors”. This additional element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (processor) (See MPEP 2106.05(f)).
“a non-transitory computer-readable medium communicatively coupled to the one or more processors and storing program code executable by the one or more processors implementing a behavior prediction system configured to predict a behavior of a user with respect to an obligation”. This additional element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (memory) (See MPEP 2106.05(f)).
“a natural language processing (NLP) layer”. This additional element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (neural network layer) (See MPEP 2106.05(f)).
“a concatenation layer”. This additional element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (neural network layer) (See MPEP 2106.05(f)).
“a set of trained machine-learning models comprising deep neural networks”. This additional element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning models) (See MPEP 2106.05(f)).
“the set of trained machine-learning models being configured to receive the user input vector and generate an ensembled output predicting the behavior of the user with respect to the obligation”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e., pre-solution activity of gathering data for use in the claimed process.
“each trained machine-learning model of the set of trained machine-learning models including a kernel density estimator configured to output a respective probability vector, wherein the set of trained machine-learning models are configured to generate the ensembled output based on the respective probability vectors outputted by the kernel density estimators of the trained machine-learning models in the set of trained machine-learning models”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (kernel density) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly, at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “A system, comprising: one or more processors”, “a non-transitory computer-readable medium communicatively coupled to the one or more processors and storing program code executable by the one or more processors implementing a behavior prediction system configured to predict a behavior of a user with respect to an obligation”, “a natural language processing (NLP) layer”, “a concatenation layer”, and “a set of trained machine-learning models comprising deep neural networks” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using a generic computer component (processor, memory, and machine learning models) (See MPEP 2106.05(f)).
The additional element(s) of “each trained machine-learning model of the set of trained machine-learning models including a kernel density estimator configured to output a respective probability vector, wherein the set of trained machine-learning models are configured to generate the ensembled output based on the respective probability vectors outputted by the kernel density estimators of the trained machine-learning models in the set of trained machine-learning models” is/are directed to a particular field of use (kernel density) (MPEP 2106.05(h)) and therefore do(es) not integrate the abstract idea into practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
As also discussed above, the additional element(s) of “the set of trained machine-learning models being configured to receive the user input vector and generate an ensembled output predicting the behavior of the user with respect to the obligation” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 2:
Step 2A: Prong 1 analysis:
Claim 2 recites in part:
“parse the unstructured user data using one or more parsing templates”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses traversing lines of text looking for specific keywords or phrases.
“generate a plurality of integer vectors”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses creating vectors containing integers as components.
“reduce, for each integer vector of the plurality of integer vectors, the dimensionality”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation. For example, dimensionality reduction can be done via PCA or other known techniques.
“generate, based on an output of the classifier model, a classification of the transaction associated with the reduced-dimensionality integer vector”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses determining what grouping a transaction belongs to.
“categorize each classification generated by the classifier model into a feature group of the one or more feature groups, each feature group of the one or more feature groups being represented by a feature group”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses determining what grouping a classification belongs to.
“generate a histogram representing the one or more feature groups”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses creating a histogram given specific data.
“and detect a pattern of one feature group relative to another feature group of two or more feature groups”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses finding a pattern within the data.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“extract transaction data based on a result of parsing the unstructured user data”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e., pre-solution activity of gathering data for use in the claimed process.
“wherein the transaction data includes a plurality of text strings, and each text string of the plurality of text strings represents a transaction associated with the user”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (transaction data) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“each integer vector of the plurality of integer vectors being generated by inputting a text string of the plurality of text strings into a trained word-to-vector model, and each integer vector of the plurality of integer vectors having a dimensionality”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (word to vector models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“using a feature extraction model”. This additional element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (extraction model) (See MPEP 2106.05(f)).
“input each reduced-dimensionality integer vector into a classifier model”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (training a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
Accordingly, at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B Analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As also discussed above, the additional element(s) of “extract transaction data based on a result of parsing the unstructured user data” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The additional element(s) of “wherein the transaction data includes a plurality of text strings, and each text string of the plurality of text strings represents a transaction associated with the user” and “each integer vector of the plurality of integer vectors being generated by inputting a text string of the plurality of text strings into a trained word-to-vector model, and each integer vector of the plurality of integer vectors having a dimensionality” is/are directed to particular field(s) of use (transaction data and word to vector models) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
As discussed above, the additional element(s) of “using a feature extraction model” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
As discussed above, the additional element(s) of “input each reduced-dimensionality integer vector into a classifier model” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome (training a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 3:
Step 2A: Prong 1 analysis:
Claim 3 recites in part:
“normalize each feature group of the one or more feature groups”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation. For example, normalization is done by manipulating the data in various ways so as to make the values fall between 0 and 1.
“generating a normalization parameter for each feature group of the one or more feature groups”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses finding any parameter that would normalize the data.
“generating a scaled feature group for each feature group of the one or more feature groups by multiplying the feature group associated with the feature group by the normalization parameter generated for the feature group, wherein a first normalization parameter for a first feature group is different from a second normalization parameter for a second feature group”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation. For example, this limitation encompasses multiplying a vector by a previously determined parameter.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
This claim recites the additional element of
“a normalization layer configured to normalize the one or more feature groups”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (neural network layers) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“the normalization parameter for each feature group being generated using a reinforcement-learning model”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (reinforcement learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B Analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “a normalization layer configured to normalize the one or more feature groups” and “the normalization parameter for each feature group being generated using a reinforcement-learning model” is/are directed to particular field(s) of use (neural network layers and reinforcement learning) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 4:
Step 2A: Prong 1 analysis:
Claim 4 recites in part:
“parse the bank statement using the one or more parsing templates”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses traversing lines of text looking for specific keywords or phrases.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 Analysis:
This claim recites the additional elements of
“access a bank statement including one or more transactions performed by the user, each transaction of the one or more transactions including a text string of the plurality of text strings, wherein each text string of the plurality of text strings represents the transaction and an amount associated with the transaction”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e., pre-solution activity of gathering data for use in the claimed process.
“extract the transaction data from one or more regions of the bank statement based on a result of the parsing”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e., pre-solution activity of gathering data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B Analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As also discussed above, the additional element of “access a bank statement including one or more transactions performed by the user, each transaction of the one or more transactions including a text string of the plurality of text strings, wherein each text string of the plurality of text strings represents the transaction and an amount associated with the transaction” and “extract the transaction data from one or more regions of the bank statement based on a result of the parsing” is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 5:
Step 2A: Prong 1 analysis:
Claim 5 recites in part:
“tune one or more hyperparameters of the feature extraction model by executing a block coordinate descent technique”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation. For example, this limitation encompasses modifying the hyperparameter(s) by utilizing the coordinate descent technique.
“reduce a dimensionality of the user input vector”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation. For example, this limitation encompasses dimensionality reduction which can be done via PCA or other known techniques.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 Analysis:
This claim recites the additional elements of:
“train a feature extraction model over a first training time period”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (training a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
“train the feature extraction model over a second training time period, wherein the tuning of the one or more hyperparameters of the feature extraction model reduces the second training time period to be smaller than the first training time period”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (training a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
“by inputting the user input vector into the trained feature extraction model associated with the one or more tuned hyperparameters”. This additional element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (extraction model) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B Analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “train a feature extraction model over a first training time period” and “train the feature extraction model over a second training time period, wherein the tuning of the one or more hyperparameters of the feature extraction model reduces the second training time period to be smaller than the first training time period” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome (training a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
As discussed above, the additional element(s) of “by inputting the user input vector into the trained feature extraction model associated with the one or more tuned hyperparameters” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 6:
Step 2A: Prong 2 analysis:
This claim recites the additional elements of:
“wherein the kernel density estimator includes one or more coefficient parameters configured using a grid search technique or a coordinate block descent technique”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (kernel density estimation) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B Analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the kernel density estimator includes one or more coefficient parameters configured using a grid search technique or a coordinate block descent technique” is/are directed to particular field(s) of use (kernel density estimation) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 7:
Step 2A: Prong 2 Analysis:
This claim recites the additional elements of
“extract an attribute characterizing the user from the unstructured user data, wherein the unstructured user data is an electronic document”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e., pre-solution activity of gathering data for use in the claimed process.
“retrieve additional attribute data associated with the attribute from an external database”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e., pre-solution activity of gathering data for use in the claimed process.
“extract a feature from the additional attribute data, the feature being included in the user input vector”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e., pre-solution activity of gathering data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B Analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As also discussed above, the additional element of “extract an attribute characterizing the user from the unstructured user data, wherein the unstructured user data is an electronic document”, “retrieve additional attribute data associated with the attribute from an external database” and “extract a feature from the additional attribute data, the feature being included in the user input vector” is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 8:
Due to claim language similar to that of Claim 1, Claim 8 is rejected for the same reasons presented above in the rejection of Claim 1.
Regarding Claim 9:
Due to claim language similar to that of Claim 2, Claim 9 is rejected for the same reasons presented above in the rejection of Claim 2.
Regarding Claim 10:
Due to claim language similar to that of Claim 3, Claim 10 is rejected for the same reasons presented above in the rejection of Claim 3.
Regarding Claim 11:
Due to claim language similar to that of Claim 4, Claim 11 is rejected for the same reasons presented above in the rejection of Claim 4.
Regarding Claim 12:
Due to claim language similar to that of Claim 5, Claim 12 is rejected for the same reasons presented above in the rejection of Claim 5.
Regarding Claim 13:
Due to claim language similar to that of Claim 6, Claim 13 is rejected for the same reasons presented above in the rejection of Claim 6.
Regarding Claim 14:
Due to claim language similar to that of Claim 7, Claim 14 is rejected for the same reasons presented above in the rejection of Claim 7.
Regarding Claim 15:
Due to claim language similar to that of Claims 1 and 8, Claim 15 is rejected for the same reasons presented above in the rejection of Claims 1 and 8.
Regarding Claim 16:
Step 2A: Prong 1 analysis:
Claim 5 recites in part:
“generate an initial probability vector corresponding to the user”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses creating a probability vector associated with a user.
“generate a normalized probability vector by normalizing the initial probability vector, wherein the normalized probability vector serves as the respective probability vector used to generate the ensembled output”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses normalizing a vector.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 17:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the ensembled output is generated based on a combination of all of the respective probability vectors outputted by all of the kernel density estimators in all of the trained machine-learning models in the set of trained machine-learning models”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (kernel density) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly, at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the ensembled output is generated based on a combination of all of the respective probability vectors outputted by all of the kernel density estimators in all of the trained machine-learning models in the set of trained machine-learning models” is/are directed to a particular field of use (kernel density) (MPEP 2106.05(h)) and therefore do(es) not integrate the abstract idea into practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 18:
Due to claim language similar to that of Claims 6 and 13, Claim 18 is rejected for the same reasons presented above in the rejections of Claims 6 and 13.
Regarding Claim 19:
Due to claim language similar to that of Claims 5 and 12, Claim 19 is rejected for the same reasons presented above in the rejections of Claims 5 and 12.
Regarding Claim 20:
Due to claim language similar to that of Claims 7 and 14, Claim 20 is rejected for the same reasons presented above in the rejections of Claims 7 and 14.
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.
Claim(s) 1, 7, 8, 14, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allen et al (US 20190066020 A1, hereinafter Allen), in view of Smith et al (US 6993193 B2, hereinafter Smith), and in view of Kazi et al (US 20220172040 A1, hereinafter Kazi).
Regarding Claim 1:
Allen teaches
A system, comprising: one or more processors; (Allen [0034]: “According to some embodiments, the memory 440 comprises logic (e.g., instructions or applications) 445 that can be executed by the processor 430 to perform various methods”)
and a non-transitory computer-readable medium communicatively coupled to the one or more processors and storing program code executable by the one or more processors (Allen [0034]: “According to some embodiments, the memory 440 comprises logic (e.g., instructions or applications) 445 that can be executed by the processor 430 to perform various methods”)
implementing a behavior prediction system configured to predict a behavior of a user with respect to an obligation (Allen [0037]: “In some embodiments the system 405 may be configured to derive a score (or set of scores) that can be used to predict entrepreneurial behavior and success-potential of a Business Person”; (EN): “entrepreneurial behavior” reads on “with respect to an obligation” as there are many attributes to this behavior. Fig. 6 element 640 presents a non-exhaustive list of attributes)
a natural language processing (NLP) layer configured to extract feature groups associated with the user from unstructured user data associated with the user (Allen [0079]: “Next, the method includes a step 610 where features are extracted from the entrepreneur data.”; [0103]: “These other processes extract features form the large volume of resulting data. Features can be extracted in a feature extraction layer 816.”; [Fig. 6, 610]: Fig. 6 element 610/b shows the feature extraction step in the diagram; (EN): it is noted in applicant’s disclosure that the NLP layer is “configured to extract one or more feature groups associated with the user from unstructured user data associated with the user”).
a concatenation layer configured to generate a user input vector using the unstructured user data, the user input vector representing the feature groups associated with the user (Allen [0137]: “the methods disclosed herein include determining Independent Variables (IV) that form inputs. These IV are processed into a vector representation and weighted according to methods disclosed. In general, the IV can include any of the entrepreneur data disclosed herein and/or combinations of entrepreneur data and business event data. The IV can also include extracted components of the entrepreneur data”; (EN): “independent variables” reads on “feature groups”);
the set of trained machine-learning models being configured to receive the user input vector and generate an ensembled output predicting the behavior of the user with respect to the obligation (Allen [0030]: “FIG. 2 is a diagram of a process for extracting features from the categorized databases, providing these features to predictive models (either mathematically derived or qualitatively derived), which then produces scores relating to the entrepreneurial success in question”; [0178]: “whereby machine learning is implemented to continually (or periodically) collect IV over time and re-perform the method of FIG. 10 on an ongoing basis to learn additional variances”)
Allen does not distinctly disclose
each trained machine-learning model of the set of trained machine-learning models including a kernel density estimator configured to output a respective probability vector, wherein the set of trained machine-learning models are configured to generate the ensembled output based on the respective probability vectors outputted by the kernel density estimators of the trained machine-learning models in the set of trained machine-learning models.
However, the combination of Allen and Smith teaches
each trained machine-learning model of the set of trained machine-learning models including a kernel density estimator configured to output a respective probability vector, wherein the set of trained machine-learning models are configured to generate the ensembled output based on the respective probability vectors outputted by the kernel density estimators of the trained machine-learning models in the set of trained machine-learning models (Allen [0030]: “FIG. 2 is a diagram of a process for extracting features from the categorized databases, providing these features to predictive models (either mathematically derived or qualitatively derived), which then produces scores relating to the entrepreneurial success in question”; [0178]: “whereby machine learning is implemented to continually (or periodically) collect IV over time and re-perform the method of FIG. 10 on an ongoing basis to learn additional variances”; Smith [Col 15, line 66 – Col 16, line 1]: “Evaluating 150 the unknown object still further comprises calculating 156 the probability density of the feature data for the unknown object represented in the rotated vector A' ”; (EN): “object” reads on “user” and the feature data of an object is analogous to the feature groups of a user).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the Machine learning and adaptive multi-variable assessment systems and methods for predicting entrepreneurial behavior of Allen with the method and system of object classification of Smith in order to provide a method for density estimation of feature data of an object to be classified (Smith [Col 15, line 66 – Col 16, line 1]: “Evaluating 150 the unknown object still further comprises calculating 156 the probability density of the feature data for the unknown object represented in the rotated vector A' ”)
Allen + Smith does not distinctly disclose
a set of trained machine-learning models comprising deep neural networks
However, Kazi teaches
a set of trained machine-learning models comprising deep neural networks (Kazi [0051]: “An example of a neural network is a convolutional neural network (CNN), which is a class of deep neural networks. CNNs have applications in image and video recognition, recommender systems, image classification, medical image analysis, natural language processing, and financial time series.”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the machine learning and adaptive multi-variable assessment systems and methods for predicting entrepreneurial behavior of Allen + Smith with the techniques for training a machine-learned model based on feedback of Kazi in order to provide a set of deep learning models that can be applied to a user’s financial history as presented in the instant application (Kazi [0051]: “An example of a neural network is a convolutional neural network (CNN), which is a class of deep neural networks. CNNs have applications in image and video recognition, recommender systems, image classification, medical image analysis, natural language processing, and financial time series.”)
Regarding Claim 7:
Allen teaches
extract an attribute characterizing the user from the unstructured user data, wherein the unstructured user data is an electronic document (Allen [0027]: “the present disclosure provides methods and systems for capturing as many of a plurality of types of information about entrepreneurs and their communications as possible (especially electronic data gathered from emails, websites, forums, blogs, and so forth). The present disclosure also provides systems and methods for extracting measures and/or features of the information“; [0054]: “In some embodiments, the system 405 collects information (e.g., entrepreneur data) using electronic data gathering techniques and stores the information as unstructured data”)
retrieve additional attribute data associated with the attribute from an external database (Allen [0100]: “the Go-lang API 806 initiates a process Get Gigya data 808—which is a third party aggregator of FaceBook™, LinkedIn™ and Twitter™ data (as well as other social-media data). These data are collected and stored to a database”, [0103]: “In addition to these data collection steps, additional processes are triggered that scan the data resulting from the above-described process. These other processes extract features form the large volume of resulting data”)
extract a feature from the additional attribute data, the feature being included in the user input vector (Allen [0137]: “These IV (independent variables) are processed into a vector representation and weighted according to methods disclosed. In general, the IV can include any of the entrepreneur data disclosed herein and/or combinations of entrepreneur data and business event data. The IV can also include extracted components of the entrepreneur data, as disclosed above”)
Regarding Claim 8:
Due to claim language similar to that of Claim 1, Claim 8 is rejected for the same reasons presented above in the rejection of Claim 1.
Regarding Claim 14:
Due to claim language similar to that of Claim 7, Claim 14 is rejected for the same reasons presented above in the rejection of Claim 7.
Regarding Claim 15:
Due to claim language similar to that of Claims 1 and 8, Claim 15 is rejected for the same reasons presented above in the rejection of Claims 1 and 8.
Regarding Claim 20:
Due to claim language similar to that of Claims 7 and 14, Claim 20 is rejected for the same reasons presented above in the rejections of Claims 7 and 14.
Claim Rejections - 35 USC § 103
Claim(s) 2 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allen, Smith, and Kazi as applied to claims 1, 8, and 15 above, and further in view of Soni et al (US 20180285459 A1, hereinafter Soni) and Dilip et al (US 20180232650 A1, hereinafter Dilip).
Regarding Claim 2:
Allen teaches
The system of claim 1, wherein the NLP layer is further configured to: parse the unstructured user data using one or more parsing templates (Allen [0053]: “the system 405 is configured to parse this data out into facets that can be used in transaction related processes”; [0054]: “In some embodiments, the system 405 collects information (e.g., entrepreneur data) using electronic data gathering techniques and stores the information as unstructured data”);
extract transaction data based on a result of parsing the unstructured user data, wherein the transaction data includes a plurality of text strings, and each text string of the plurality of text strings represents a transaction associated with the user (Allen [0053]: “the system 405 is configured to parse this data out into facets that can be used in transaction related processes”; [0077]: “the system 405 can gather actual transaction risk metrics. For example, the system 405 can determine the actual variations in payment amount, timing, and so forth for purchaser type and for product type”);
categorize each classification generated by the classifier model into a feature group of the one or more feature groups, each feature group of the one or more feature groups being represented by a feature group ; (Allen [0058]: “The system 405 can categorize an individual's relationships, for example, by region, by economic development of location, and so forth, and distributions of categorized friends and reach across physical space and economic distance factor into diversification measures”; (EN): the examples of “an individual’s relationships” reads on “feature groups”);
Allen does not distinctly disclose
reduce, for each integer vector of the plurality of integer vectors, the dimensionality using a feature extraction model;
input each reduced-dimensionality integer vector into a classifier model;
generate, based on an output of the classifier model, a classification of the transaction associated with the reduced-dimensionality integer vector;
and detect a pattern of one feature group relative to another feature group of two or more feature groups.
However, Smith teaches
reduce, for each integer vector of the plurality of integer vectors, the dimensionality using a feature extraction model (Smith [Col 7 lines 55-57]: “the result of performing 120 PCA is a first reduction of the dimensionality of the problem”; [Fig. 2]: Fig. 2, 120 is a general process of performing PCA for dimensionality reduction);
input each reduced-dimensionality integer vector into a classifier model (Smith [Col 16 lines 4-7]: “Preferably, determining 158 uses Bayes' Theorem. Bayes' Theorem is well known in the art as a way of reversing the direction of probabilistic (i.e., conditional) statements”; [Fig. 4]: Fig. 4, 158 is the “determine class membership” step of the process, which utilizes Bayes Theorem, as stated above);
generate, based on an output of the classifier model, a classification of the transaction associated with the reduced-dimensionality integer vector (Smith [Col 16 lines 4-7]: “Preferably, determining 158 uses Bayes' Theorem. Bayes' Theorem is well known in the art as a way of reversing the direction of probabilistic (i.e., conditional) statements”; [Fig. 4]: Fig. 4, 158 is the “determine class membership” step of the process, which utilizes Bayes Theorem, as stated above);
and detect a pattern of one feature group relative to another feature group of two or more feature groups (Smith [Col 1 lines 45-48]: “The object features are compared to sets of representative features for each of the possible groups and a determination is made based on an aggregate of the comparison results”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the Machine learning and adaptive multi-variable assessment systems and methods for predicting entrepreneurial behavior of Allen with the method and system of object classification of Smith in order to provide a method for density estimation of feature data of an object to be classified (Smith [Col 15, line 66 – Col 16, line 1]: “Evaluating 150 the unknown object still further comprises calculating 156 the probability density of the feature data for the unknown object represented in the rotated vector A' ”)
Allen + Smith + Kazi does not distinctly disclose
generate a plurality of integer vectors, each integer vector of the plurality of integer vectors being generated by inputting a text string of the plurality of text strings into a trained word-to-vector model, and each integer vector of the plurality of integer vectors having a dimensionality;
However, Soni teaches
generate a plurality of integer vectors, each integer vector of the plurality of integer vectors being generated by inputting a text string of the plurality of text strings into a trained word-to-vector model, and each integer vector of the plurality of integer vectors having a dimensionality (Soni [0044], Fig. 2A, 104, 206, 208: “DocTag2Vec embeds documents, words, and labels in a same vector space such that a document and its associated labels are embedded in close proximity to each other”; [Fig. 2A]: Fig. 2A, 104, 206, and 208 are representations of documents and tag sets being mapped into the same vector space via DocTag2Vec).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the machine learning and adaptive multi-variable assessment systems and methods for predicting entrepreneurial behavior of Allen + Smith with the methods and systems for multilabel learning via supervised joint embedding of documents and labels of Soni in order to provide a method for word-to-vector transformation that can be applied to parsed user data (Soni [0044], Fig. 2A, 104, 206, 208: “DocTag2Vec embeds documents, words, and labels in a same vector space such that a document and its associated labels are embedded in close proximity to each other”; [Fig. 2A]: Fig. 2A, 104, 206, and 208 are representations of documents and tag sets being mapped into the same vector space via DocTag2Vec)
Allen + Smith + Kazi + Soni does not distinctly disclose
generate a histogram representing the one or more feature groups;
However, Dilip teaches
generate a histogram representing the one or more feature groups (Dilip [0012]: “FIG. 12 is a flowchart illustrating the generation of a histogram in accordance with one embodiment”; [Fig. 12]: Fig. 12 shows the general flowchart of histogram generation; (EN): generating the histogram is not dependent on any of the previous steps of the claim);
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the entrepreneurial behavior prediction system of Allen + Smith + Kazi + Soni with the histogram generation of Dilip in order to develop models with less stored parameters and higher prediction accuracy (Dilip [0046]: “PW estimators serve as empirical distribution functions (or generalized histograms), and the goal is to develop models with lesser stored parameters and higher prediction accuracy”).
Regarding Claim 9:
Due to claim language similar to that of Claim 2, Claim 9 is rejected for the same reasons presented above in the rejection of Claim 2.
Claim Rejections - 35 USC § 103
Claim(s) 3 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allen, Smith, and Kazi as applied to claims 1, 8, and 15 above, and further in view of Sermanet (US 20190332920 A1).
Regarding Claim 3:
Smith teaches
The system of claim 2, wherein the behavior prediction system is configured to further comprise: a normalization layer configured to normalize the one or more feature groups by: normalize each feature group of the one or more feature groups (Smith [Col 15, lines 24-26]: “evaluating 150 comprises normalizing and centralizing 152 the measured feature data for the unknown object”; [Fig. 4]: 150 represents the process of classifying an unknown object, 152 represents the normalization step in this process; (EN): Smith mentions in the disclosure that “The measurements and/or combinations of the measurements can be thought of as `features` of the object”, this is analogous to feature groups);
generating a normalization parameter for each feature group of the one or more feature groups (Smith [Col 15, lines 34-37]: “normalizing and centralizing 152 preferably involves spherizing the data and thus creates a vector A' by subtracting a mean value from each element Aj in a vector A of the measured data for the unknown object”)
generating a scaled feature group for each feature group of the one or more feature groups by multiplying the feature group associated with the feature group by the normalization parameter generated for the feature group, wherein a first normalization parameter for a first feature group is different from a second normalization parameter for a second feature group (Smith [Col 15, lines 38-50], equation (13): equation (13) shows the normalization parameter as applied to every member of vector Aj).
Smith does not distinctly disclose
the normalization parameter for each feature group being generated using a reinforcement-learning model;
However, Sermanet teaches
the normalization parameter for each feature group being generated using a reinforcement-learning model (Sermanet [0023]: “A task partitioning engine 150 determines a partitioning of the reinforcement learning task into subtasks”, [0040]: “The system then computes, for each subtask, distribution statistics, e.g., a mean and a standard deviation, for normalized feature values”);
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the entrepreneurial behavior prediction system of Allen + Smith + Kazi + Soni with the reinforcement learning model of Sermanet in order to allow the system to identify the highest-scoring features of the reinforcement task (Sermanet [0042]: “The system can then select, for each subtask, a fixed number of highest-scoring features as the discriminative features for the subtask”).
Regarding Claim 10:
Due to claim language similar to that of Claim 3, Claim 10 is rejected for the same reasons presented above in the rejection of Claim 3.
Claim Rejections - 35 USC § 103
Claim(s) 4 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allen, Smith, and Kazi as applied to claims 1, 8, and 15 above, and further in view of Stroh (US 20120265655 A1).
Regarding Claim 4:
Allen + Smith + Kazi does not distinctly disclose
The system of claim 2, wherein the NLP layer is further configured to: access a bank statement including one or more transactions performed by the user, each transaction of the one or more transactions including a text string of the plurality of text strings, wherein each text string of the plurality of text strings represents the transaction and an amount associated with the transaction;
parse the bank statement using the one or more parsing templates;
extract the transaction data from one or more regions of the bank statement based on a result of the parsing.
However, Stroh teaches
The system of claim 2, wherein the NLP layer is further configured to: access a bank statement including one or more transactions performed by the user, each transaction of the one or more transactions including a text string of the plurality of text strings, wherein each text string of the plurality of text strings represents the transaction and an amount associated with the transaction (Stroh [0069]: “transaction data, including the transaction character strings and associated transaction data strings, for each single financial transaction is stored in a database”; [0070]: “Alternatively, the record generation system 12 receives a data file of transactions (e.g. CSV format file, bank statement, 3rd party accounting software file)”);
parse the bank statement using the one or more parsing templates (Stroh [0104]: “In addition to identifying patterns, the record generation subsystem 12 will have "financial statement" transaction data (Statement Data) parsed out of the CCS (complete character string) and stored separately”);
extract the transaction data from one or more regions of the bank statement based on a result of the parsing (Stroh [0114]: “Having inserted transaction markers throughout the remaining CCS, the record generation subsystem 12 parses the string into individual transactions substrings”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the entrepreneurial behavior prediction system of Allen + Smith + Kavi with the system for processing a transaction document of Stroh in order to include a method for parsing a user’s transactional data and financial records (Stroh [0015]: “It would therefore be desirable to provide an automated accounting system for processing transaction documents including one or financial transaction entries which ameliorates or overcomes any one or more of these difficulties. It would also be desirable to provide an automated accounting system which is simple, efficient, accurate and/or minimised the requirement for human intervention”)
Regarding Claim 11:
Due to claim language similar to that of Claim 4, Claim 11 is rejected for the same reasons presented above in the rejection of Claim 4.
Claim Rejections - 35 USC § 103
Claim(s) 5, 6, 12, 13, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allen, Smith, Kazi, and Soni as applied to claims 1, 8, and 15 above, and further in view of Chen et al (US 20190197013 A1, hereinafter Chen).
Regarding Claim 5:
Smith teaches
reduce a dimensionality of the user input vector by inputting the user input vector into the trained feature extraction model associated with the one or more tuned hyperparameters (Smith [Col 7 lines 55-57], Fig. 2, 120: “the result of performing 120 PCA is a first reduction of the dimensionality of the problem”; [Fig. 2]: Fig. 2, 120 shows the general process of performing PCA on the data; (EN): PCA is an unsupervised model)
Allen + Smith + Kazi does not distinctly disclose
The system of claim 1, wherein the behavior prediction system is configured to further comprise a feature extraction layer, wherein the feature extraction layer is configured to: train a feature extraction model over a first training time period;
train the feature extraction model over a second training time period, wherein the tuning of the one or more hyperparameters of the feature extraction model reduces the second training time period to be smaller than the first training time period;
However, Soni teaches
The system of claim 1, wherein the behavior prediction system is configured to further comprise a feature extraction layer, wherein the feature extraction layer is configured to: train a feature extraction model over a first training time period (Soni [0003]: “It is noted that feature vectors are generated separately before training”; [0083]: “For the remaining hyperparameters, grid search was applied to find the best ones”);
train the feature extraction model over a second training time period, wherein the tuning of the one or more hyperparameters of the feature extraction model reduces the second training time period to be smaller than the first training time period (Soni [0060]: “The SGD training supports the incremental adjustment of DocTag2Vec to new data. The prediction process can utilize a relatively simple k-nearest neighbor search among tags, rather than documents, whose run time does not scale up effectively as training data increases”; [0083]: “For the remaining hyperparameters, grid search was applied to find the best ones”; (EN): incremental adjustment of training is used in lieu of training on a new dataset due to runtime limitations);
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the machine learning and adaptive multi-variable assessment systems and methods for predicting entrepreneurial behavior of Allen + Smith with the methods and systems for multilabel learning via supervised joint embedding of documents and labels of Soni in order to provide a method for word-to-vector transformation that can be applied to parsed user data (Soni [0044], Fig. 2A, 104, 206, 208: “DocTag2Vec embeds documents, words, and labels in a same vector space such that a document and its associated labels are embedded in close proximity to each other”; [Fig. 2A]: Fig. 2A, 104, 206, and 208 are representations of documents and tag sets being mapped into the same vector space via DocTag2Vec)
Allen + Smith + Kazi + Soni does not distinctly disclose
tune one or more hyperparameters of the feature extraction model by executing a block coordinate descent technique;
However, Chen teaches
tune one or more hyperparameters of the feature extraction model by executing a block coordinate descent technique (Chen [0018]: “FIG 8 is a flow diagram illustrating a method for parallelized block coordinate descent in accordance with an example embodiment”; [Fig. 8]: Fig. 8, 800 shows the general process, via flowchart, of performing a block coordinate descent operation)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the entrepreneurial behavior prediction system of Allen + Smith + Kazi + Soni with the iterative training process for machine learning models of Chen in order to overcome the bottleneck of scalability when the model(s) are trained on larger datasets (Chen [0009]: “However, in scenarios where data is abundant having a more fine-grained model at the user or item level would potentially lead to more accurate prediction, as the user's personal preferences on items and the item's specific attraction for users could be better captured”; [0023]: “the scalability bottleneck is overcome by applying parallelized block coordinate descent”)
Regarding Claim 6:
Allen + Smith + Kazi + Soni does not distinctly disclose
The system of claim 1, wherein the kernel density estimator includes one or more coefficient parameters configured using a grid search technique or a coordinate block descent technique.
However, Chen teaches
The system of claim 1, wherein the kernel density estimator includes one or more coefficient parameters configured using a grid search technique or a coordinate block descent technique (Chen [0018]: “FIG 8 is a flow diagram illustrating a method for parallelized block coordinate descent in accordance with an example embodiment”; [Fig. 8]: Fig. 8, 800 shows the general process, via flowchart, of performing a block coordinate descent operation).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the entrepreneurial behavior prediction system of Allen + Smith + Kazi + Soni with the iterative training process for machine learning models of Chen in order to overcome the bottleneck of scalability when the model(s) are trained on larger datasets (Chen [0009]: “However, in scenarios where data is abundant having a more fine-grained model at the user or item level would potentially lead to more accurate prediction, as the user's personal preferences on items and the item's specific attraction for users could be better captured”; [0023]: “the scalability bottleneck is overcome by applying parallelized block coordinate descent”)
Regarding Claim 12:
Due to claim language similar to that of Claim 5, Claim 12 is rejected for the same reasons presented above in the rejection of Claim 5.
Regarding Claim 13:
Due to claim language similar to that of Claim 6, Claim 13 is rejected for the same reasons presented above in the rejection of Claim 6.
Regarding Claim 19:
Due to claim language similar to that of Claims 5 and 12, Claim 19 is rejected for the same reasons presented above in the rejections of Claims 5 and 12.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20200302524 A1 – Systems and methods for training models to improve fairness
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/COREY SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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