DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claim 1-3, 5-12, and 14-20, are presented for examination. Applicant filed a request for continued examination on 08/03/2026 amending claims 1, 5, 7, 10, 11, 14, 16, 19, and 20. In light of Applicant’s amendments, Examiner has withdrawn the previous objections of claims 1, 5, 7, 11, 14, 16, and 20; and the previous § 101 rejection of claims 1-3, 5-12, and 14-20. Examiner has, however, established new objections for claims 1, 7, 11, and 16; and new § 101 rejection of claims 1-3, 5-12, and 14-20, in the instant Office action.
Examiner’s Remarks
Patent Eligibility under § 101:
Applicant argues in pages 22-24 of Applicant’s Remarks:
The amended claims do not recite a financial transaction, payment authorization, settlement operation, transfer of funds, or any other business interaction between parties. Rather, the claims recite a specific machine-executed workflow for processing unstructured electronic mails and attached electronic documents to identify and classify information contained therein.
Importantly, the amended claims now expressly recite (i) the data used by the machine learning model, namely information tokens, feature vectors, and training data associated with electronic mails and attached electronic documents; (ii) how the machine learning model is trained, namely by correlating feature vectors associated with payment-information features with payment amounts and payment identifiers; and (iii) how the machine learning model evolves, namely through re-training over a plurality of time intervals based on the training data. Accordingly, the amended claims provide the technical details regarding the training data, the training process, and the evolution of the machine learning model that were identified by the Examiner as lacking.
Examiner respectfully disagrees:
First, the preamble of independent claim 1 recites: “A machine-learning based (ML-based) computing method for determining one or more payment information from one or more electronic mails.” Therefore, Examiner found that instant claims fall under certain methods of organizing human activity (i.e., commercial interactions) group.
Second, instant claims are still lacking the on-going evolution of the machine learning model. Applicant has amended independent claims 1, 11, and 20, to recite:
re-training the machine learning model over a plurality of time intervals based on one or more training data associated with at least one of: the one or more electronic mails and the one or more electronic documents attached in the one or more electronic mails;
“Re-training the machine learning model over a plurality of time intervals,” however, does not imply on-going evolution, but instead that there is a “plurality of time intervals” after which the re-training stops.
Therefore, newly amended claims are not patent eligible under § 101. Applicant is invited to arrange an interview with Examiner to discuss this issue further.
Prior Art under § 103: The closest prior art reference of record – Lindeman (US 10,748,134 B2) discloses:
[R]eceiving a token including information relating to a payee, the information including payment information enabling processing of a transaction to a financial account of the payee; storing the token or extracted payment information from the token in association with the management application; and transferring the payment information to a payment application operating on the mobile communication device.
Lindeman, however, fails to disclose – alone or in combination with other references – the limitations of independent claims 1, 11, and 20, as an ordered combination of steps.
Claim Objections
Claim 1 is objected to because of the following informality:
determining, by the one or more hardware processors, one or more payment information features for the one or more information tokens by analyzing one or more contexts of the one or more information tokens based on the positions of the tokens resulting from the indexing, wherein the one or more payment information features are configured to determine whether the one or more information tokens comprise one or more contents related to one or more first payment information, wherein the one or more first payment information comprise at least one of: one or more payment amounts and one or more payment identifiers;
The word “and” should appear before last “wherein” clause. Applicant could amend claim 1 to recite:
determining, by the one or more hardware processors, one or more payment information features for the one or more information tokens by analyzing one or more contexts of the one or more information tokens based on the positions of the tokens resulting from the indexing, wherein the one or more payment information features are configured to determine whether the one or more information tokens comprise one or more contents related to one or more first payment information, and wherein the one or more first payment information comprise at least one of: one or more payment amounts and one or more payment identifiers;
Claim 7 is objected to because of the following informality:
segmenting, by the one or more hardware processors, the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets;, wherein the max_depth hyperparameter is configured to control an optimum depth of each decision tree in the random forest based machine learning model, wherein the class weight hyperparameter is configured to adjust one or more second weights of one or more classes in the random forest based machine learning model to control one or more class imbalance errors, wherein the n_estimators hyperparameter is configured to indicate a number of one or more decision trees to be included in the random forest based machine learning model, wherein the min_samples_split hyperparameter is configured to set a pre-determined number of one or more data points required in a node before the one or more data points split during a tree-building process, wherein the max_features hyperparameter is configured to determine an optimum number of the one or more payment information features when the optimum split of the one or more payment information features at each node in the random forest based machine learning model, and wherein the min_samples_leaf is configured to indicate the pre-determined number of one or more data points required to generate a leaf node during the tree-building process;
Currently there are two punctuation marks – comma (“,”) and semi-colon (“;”) – between the words “datasets” and “wherein.” The semi-colon should be deleted. Applicant could amend claim 7 to recite:
segmenting, by the one or more hardware processors, the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets[[;]], wherein the max_depth hyperparameter is configured to control an optimum depth of each decision tree in the random forest based machine learning model, wherein the class weight hyperparameter is configured to adjust one or more second weights of one or more classes in the random forest based machine learning model to control one or more class imbalance errors, wherein the n_estimators hyperparameter is configured to indicate a number of one or more decision trees to be included in the random forest based machine learning model, wherein the min_samples_split hyperparameter is configured to set a pre-determined number of one or more data points required in a node before the one or more data points split during a tree-building process, wherein the max_features hyperparameter is configured to determine an optimum number of the one or more payment information features when the optimum split of the one or more payment information features at each node in the random forest based machine learning model, and wherein the min_samples_leaf is configured to indicate the pre-determined number of one or more data points required to generate a leaf node during the tree-building process;
Claim 11 is objected to because of the following informalities:
a token extraction subsystem configured to:
extract one or more information tokens from the one or more data associated with at least one of: the one or more electronic mails and the one or more electronic documents attached in the one or more electronic mails;
filter the one or more information tokens based on at least one of: token length, frequency, and relevance;
index the one or more information tokens based on positions of the tokens in the extracted text;
. . .
a training subsystem configured to:
train a machine learning model to correlate the one or more feature vectors associated with the one or more payment information features, with at least one of: the one or more payment amounts and the one or more payment identifiers, based on one or more hyperparameters;
re-train the machine learning model over a plurality of time intervals based on one or more training data associated with at least one of: the one or more electronic mails and the one or more electronic documents attached in the one or more electronic mails;
There should be the word “and” after both “relevance;” and “hyperparameters;.” Applicant could amend claim 11 to recite:
a token extraction subsystem configured to:
extract one or more information tokens from the one or more data associated with at least one of: the one or more electronic mails and the one or more electronic documents attached in the one or more electronic mails;
filter the one or more information tokens based on at least one of: token length, frequency, and relevance; and
index the one or more information tokens based on positions of the tokens in the extracted text;
. . .
a training subsystem configured to:
train a machine learning model to correlate the one or more feature vectors associated with the one or more payment information features, with at least one of: the one or more payment amounts and the one or more payment identifiers, based on one or more hyperparameters; and
re-train the machine learning model over a plurality of time intervals based on one or more training data associated with at least one of: the one or more electronic mails and the one or more electronic documents attached in the one or more electronic mails;
Claim 16 is objected to because of the following informality:
segment the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets; wherein the max_depth hyperparameter is configured to control an optimum depth of each decision tree in the random forest based machine learning model, wherein the class_weight hyperparameter is configured to adjust the one or more second weights of one or more classes in the random forest based machine learning model to control one or more class imbalance errors, wherein the n_estimators hyperparameter is configured to indicate a number of one or more decision trees to be included in the random forest based machine learning model, wherein the min_samples_split hyperparameter is configured to set a pre-determined number of one or more data points required in a node before the one or more data points split during a tree-building process, wherein the max-features hyperparameter is configured to determine an optimum number of the one or more payment information features when the optimum split of the one or more payment information features at each node in the random forest based machine learning model, and wherein the min_samples_leaf is configured to indicate the pre-determined number of one or more data points required to generate a leaf node during the tree- building process;
There should be comma (“,”) instead of semi-colon (“;”) between “datasets” and “wherein.” Applicant could amend claim 16 to recite:
segment the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets,[[;]] wherein the max_depth hyperparameter is configured to control an optimum depth of each decision tree in the random forest based machine learning model, wherein the class_weight hyperparameter is configured to adjust the one or more second weights of one or more classes in the random forest based machine learning model to control one or more class imbalance errors, wherein the n_estimators hyperparameter is configured to indicate a number of one or more decision trees to be included in the random forest based machine learning model, wherein the min_samples_split hyperparameter is configured to set a pre-determined number of one or more data points required in a node before the one or more data points split during a tree-building process, wherein the max-features hyperparameter is configured to determine an optimum number of the one or more payment information features when the optimum split of the one or more payment information features at each node in the random forest based machine learning model, and wherein the min_samples_leaf is configured to indicate the pre-determined number of one or more data points required to generate a leaf node during the tree- building process;
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, 5-12, and 14-20, are rejected under 35 USC § 101 because they are directed to non-statutory subject matter. The rationale for this finding is explained below.
The Supreme Court in Mayo laid out a framework for determining whether an applicant is seeking to patent a judicial exception itself or a patent-eligible application of the judicial exception. See Alice Corp., 134 S. Ct. at 2355,110 USPQ2d at 1981 (citing Mayo, 566 U.S. 66, 101 USPQ2d 1961). This framework, which is referred to as the Mayo test or the Alice/Mayo test (“the test”), is described in detail in Manual of Patent Examining Procedure (”MPEP”) (see MPEP § 2106(III) for further guidance). The step 1 of the test: It need to be determined whether the claims are directed to a patent eligible (i.e., statutory) subject matter under 35 USC § 101. Step 2A of the test: If the claims are found to be directed to a statutory subject matter, the next step is to determine whether the claims are directed to a judicial exception i.e., law of nature, natural phenomenon, and abstract idea (Prong 1). If the claims are found to be directed to an abstract idea, it needs to be determined whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong 2). Step 2B of the test: If the claims are directed to a judicial exception, the next and final step is to determine whether the claims recite additional elements that amount to significantly more than the judicial exception.
Step 1 of the Test:
When considering subject matter eligibility under 35 USC § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. Here, the claimed invention of claims 1-3 and 5-10 is a series of steps, which is method (i.e., a process) and, thus, one of the statutory categories of invention. Further, the claimed invention of claims 11-12 and 14-19 is a system, which is also one of the statutory categories of invention. Still further, the claimed invention of claim 20 is a non-transitory computer-readable storage medium which is also one of the statutory categories of invention.
Conclusion of Step 1 Analysis: Therefore, claims 1-3, 5-12, and 14-20, are statutory under 35 USC § 101 in view of step 1 of the test.
Step 2A of the Test:
Prong 1: Claims 1-3, 5-12, and 14-20, however, recite an abstract idea of determining one or more payment information from one or more electronic mails. The creation of determining one or more payment information from one or more electronic mails, as recited in the independent claims 1, 11, and 20, belongs to certain methods of organizing human activity (i.e., commercial interactions) that are found by the courts to be abstract ideas. The limitations in independent claims 1, 11, and 20, which set forth or describe the recited abstract idea, are found in the following steps:
“extracting one or more information tokens from the one or more data associated at least one of: the one or more electronic mails and the one or more electronic documents attached in the one or more electronic mails” (claims 1, 11, and 20);
“filtering the one or more information tokens based on at least one of: token length, frequency, and relevance” (claims 1, 11, and 20);
“indexing the one or more information tokens based on positions of the tokens in the extracted text” (claims 1, 11, and 20);
“determining, by the one or more hardware processors, one or more payment information features for the one or more information tokens by analyzing one or more contexts of the one or more information tokens based on the positions of the tokens resulting from the indexing, wherein the one or more payment information features are configured to determine whether the one or more information tokens comprise one or more contents related to one or more first payment information, wherein the one or more first payment information comprise at least one of: one or more payment amounts and one or more payment identifiers” (claims 1, 11, and 20);
“selecting one or more optimum information tokens by analyzing the determined one or more payment information features by one or more parameter-driven pre-configured rules” (claims 1, 11, and 20);
“training a machine learning model to correlate one or more feature vectors associated with the one or more payment information features with at least one of: one or more payment amounts and one or more payment identifiers, based on one or more hyperparameters” (claims 1, 11, and 20);
“re-training the machine learning model over a plurality of time intervals based on one or more training data associated with at least one of: the one or more electronic mails and the one or more electronic documents attached in the one or more electronic mails” (claims 1, 11, and 20);
“determining the one or more first payment information comprising at least one of: the one or more payment amounts and the one or more payment identifiers within at least one of: the one or more electronic mails and the one or more electronic documents, for the one or more optimum information tokens by the trained machine learning model” (claims 1, 11, and 20);
“generating one or more confidence scores for the one or more optimum information tokens, wherein the one or more confidence scores for the one or more optimum information tokens indicate quantitative measure of the one or more first payment information comprising at least one of: the one or more payment amounts and the one or more payment identifiers, available in the one or more optimum information tokens, and wherein the one or more confidence scores are generated for the one or more optimum information tokens based on at least one of: the determined one or more payment information features and one or more first weights assigned to the one or more payment information features based on the availability of the one or more first payment information comprising at least one of: the one or more payment amounts and the one or more payment identifiers in the one or more optimum information tokens” (claims 1, 11, and 20); and
“labelling the one or more optimum information tokens to classify the one or more optimum information tokens into at least one of: the one or more payment amounts, the one or more payment identifiers and one or more non-payment information, based on the one or more confidence scores generated for the one or more optimum information tokens by one or more predetermined threshold values, wherein the one or more non- payment information are distinct from the one or more first payment information, wherein the trained machine learning model comprises a random forest based machine learning model, and wherein the one or more hyperparameters comprise at least one of: ma_depth, class_weight, n_estimators, min_samples_split, max_features, and min_ samples_leaf” (claims 1, 11, and 20).
Prong 2: In addition to abstract steps recited above in Prong 1, independent claims 1, 11, and 20, recite additional elements:
“one or more hardware processors” (claims 1 and 11);
“one or more databases” (claims 1, 11, and 20);
“a user interface associated with one or more electronic devices” (claims 1, 11, and 20);
“a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in form of programmable instructions executable by the one or more hardware processors, and wherein the plurality of subsystems comprises: a data receiving subsystem, a token extraction subsystem, a payment information feature determining subsystem, a token selection subsystem, a payment information determining subsystem, and an output subsystem” (claim 11); and
“a non-transitory computer-readable storage medium having instructions stored therein that when executed by a hardware processor, cause the processor to execute operations” (claim 20).
These additional elements are recited at a high level of generality (e.g., as a generic processor performing a generic computer functions) such that they amount to no more than mere instructions to apply the exception using a generic computer components. Further, the following limitations recite insignificant extra solution activity (for example, data gathering):
“receiving one or more data, wherein the one or more data comprise at least one of the one or more electronic mails and one or more electronic documents attached in the one or more electronic mails” (claims 1, 11, and 20); and
“providing an output of the determined one or more first payment information comprising at least one of: the one or more payment amounts and the one or more payment identifiers to one or more users” (claims 1, 11, and 20).
These additional elements/limitations do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception. The additional elements/limitations of independent claims 1, 11, and 20, here do not render improvements to the functioning of a computer or to any other technology or technical field (see MPEP § 2106.05(a)), nor do they integrate the abstract idea into a practical application under MPEP § 2106.05(b) (particular machine); MPEP § 2106.05(c) (particular transformations); or MPEP § 2106.05(e) (other meaningful limitations). Further, the combination of these additional elements/limitations is no more than mere instructions to apply the exception using a generic device. Accordingly, even in combination, these additional elements/limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Conclusion of Step 2A Analysis: Therefore, independent claims 1, 11, and 20, are non-statutory under 35 USC § 101 in view of step 2A of the test.
Step 2B of the Test: The additional elements of independent claims 1, 11, and 20, (see above under Step 2A – Prong 2) are described by Applicant’s Specification in following terms:
[0059] . . . The memory 202, the one or more hardware processors 204, and the storage unit 206 are communicatively coupled through a system bus 208 or any similar mechanism. Thememory202 includes the plurality of subsystems110 in the form of programmable instructions executable by the one or more hardware processors 204.
[0060] The plurality of subsystems 110 includes a data receiving subsystem 210, a token extraction subsystem 212. a payment information feature determining subsystem 214, a token selection subsystem 216, a payment information determining subsystem 218, an output subsystem 220, and a training subsystem 222. [].
[0061] The one or more hardware processors 204, as used herein, means any type of computational circuit, including, but not limited to. at least one of: a microprocessor unit, microcontroller, complex instruction set computing microprocessor unit. reduced instruction set computing microprocessor unit, very long instruction word microprocessor unit, explicitly parallel instruction computing microprocessor unit, graphics processing unit, digital signal processing unit, or any other type of processing circuit. The one or more hardware processors 204 may also include embedded controllers, including at least one of: generic or programmable logic devices or arrays, application specific integrated circuits, single-chip computers, and the like.
[0062] The memory 202 mac be non-transitory volatile memory and non-volatile memory. The memory 202 may be coupled for communication with the one or more hardware processors 204, being a computer-readable storage medium. The one or more hardware processors 204 may execute machine-readable instructions and/or source code stored in the memory 202. A variety of machine-readable instructions may be stored in and accessed from the memory 202. The memory 202 may include any suitable elements for storing data and machine-readable instructions, including at least one of: read only memory, random access memory, erasable programmable read only memory electrically erasable programmable read only memory, a hard drive, a removable media drive for handling compact disks, digital video disks, diskettes, magnetic tape cartridges, memory cards, and the like. In the present embodiment, the memory 202 includes the plurality of subsystems 110 stored in the form of machine-readable instructions on any of the above-mentioned storage media and may be in communication with and executed by the one or more hardware processors 204.
[0063] The storage unit 206 may be a cloud storage, a Structured Query Language (SQL) data store, a noSQL database or a location on a file system directly accessible by the plurality of subsystems 110.
0064] The plurality of subsystems 110 includes the data receiving subsystem 210 that is communicatively connected to the one or more hardware processors 204. The data receiving subsystem 210 is configured to receive the one or more data from the one or more databases 108. The one or more data include at least one of: the one or more electronic mails and the one or more electronic documents attached in the one or more electronic mails. [].
This is a description of general-purpose computer. Thus, individually, the additional elements of independent claims 1, 11, and 20, are well-understood, routine, and conventional elements that amount to no more than implementing the abstract idea with a computerized system. Further, the additional limitations of “receiving” and “providing” information amount to no more than mere instructions to apply the exception using generic computer components. For the same reason these additional limitations are not sufficient to provide an inventive concept. The additional limitations of “receiving” and “providing” information were considered as insignificant extra-solution activity in Step 2A – Prong 2. Re-evaluating here in Step 2B, they are also determined to be well-understood, routine, and conventional activity in the field. Similarly to OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network), and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), the additional limitations of independent claims 1, 11, and 20, “receive” and “provide” information over a network in a merely generic manner. The courts have recognized “receiving” and “providing” information functions as well-understood, routine and conventional when claimed in a merely generic manner. Therefore, the additional limitations of independent claims 1, 11, and 20, are well-understood, routine, and conventional. Further, taken as combination, the additional elements/limitations add nothing more than what is present when the additional elements/limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
Conclusion of Step 2B Analysis: Therefore, independent claims 1, 11, and 20, are non-statutory under 35 USC § 101 in view of step 2B of the test.
Dependent Claims: Dependent claims 2-3 and 5-10 depend on independent claim 1; and dependent claims 12 and 14-19 depend on independent claim 11. The elements in dependent claims 2-3, 5-10, 12, and 14-19, which set forth or describe the abstract idea, are:
“extracting the one or more information tokens comprises: converting, by the one or more hardware processors, the one or more data associated with at least one of the one or more electronic mails and the one or more electronic documents to one or more text formats; and transforming, by the one or more hardware processors, the one or more text formats into the one or more information tokens, based on a tokenization process” (claims 2 and 12: further narrowing the recited abstract idea);
“the one or more payment information features comprise at least one of: horizontal distance from one or more first payment keywords, vertical distance from the one or more first payment keywords, horizontal distance from one or more second payment keywords, vertical distance from the one or more second payment keywords, one or more zip codes, recurrence of the one or more information tokens, and one or more positions of the one or more information tokens” (claim 3: further narrowing the recited abstract idea);
“the one or more optimum information tokens are classified as the one or more non-payment information when the one or more confidence scores for the one or more payment amounts and the one or more payment identifiers are within the one or more predetermined threshold values; the one or more optimum information tokens are classified as the one or more payment amounts when at least one of: the one or more confidence scores for the one or more payment amounts are at least one of: equal and exceed the one or more predetermined threshold values, and the one or more confidence scores for the one or more payment identifiers are within the one or more predetermined threshold values; the one or more optimum information tokens are classified as the one or more payment identifiers when at least one of: the one or more confidence scores for the one or more payment identifiers are at least one of equal and exceed the one or more predetermined threshold values, and the one or more confidence scores for the one or more payment amounts are within the one or more predetermined threshold values; and the one or more optimum information tokens are classified as at least one of: the one or more payment amounts and the one or more payment identifiers, based on one or more first optimum confidence scores generated for at least one of: the one or more payment amounts and the one or more payment identifiers, when at least one of: the one or more confidence scores for the one or more payment identifiers and the one or more payment amounts are at least one of: equal and exceed the one or more predetermined threshold values” (claims 5 and 14: further narrowing the recited abstract idea);
“classifying, by the one or more hardware processors, the one or more optimum information tokens with one or more second optimum confidence scores related to the one or more payment amounts, as one or more optimum payment amounts, when the one or more confidence scores related to the one or more payment amounts are generated for the one or more optimum information tokens; and classifying, by the one or more hardware processors, the one or more optimum information tokens with one or more third optimum confidence scores related to the one or more payment identifiers, as one or more optimum payment identifiers, when the one or more confidence scores related to the one or more payment identifiers are generated for the one or more optimum information tokens” (claims 6 and 15: further narrowing the recited abstract idea);
“training, by the one or more hardware processors, the machine learning model, comprises: obtaining, by the one or more hardware processors, one or more labelled datasets from the one or more databases, wherein the one or more labelled datasets comprise the one or more information tokens extracted from at least one of: the one or more electronic mails and the one or more electronic documents attached in the one or more electronic mails; selecting, by the one or more hardware processors, one or more features vectors associated with the one or more payment information features for training the machine learning model based on a feature engineering process; labelling, by the one or more hardware processors, the one or more optimum information tokens to classify the one or more optimum information tokens into at least one of: the one or more payment amounts, the one or more payment identifiers and the one or more non-payment information; segmenting, by the one or more hardware processors, the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets, wherein the max_depth hyperparameter is configured to control an optimum depth of each decision tree in the random forest based machine learning model, wherein the class_weight hyperparameter is configured to adjust one or more second weights of one or more classes in the random forest based machine learning model to control one or more class imbalance errors, wherein the n_estimators hyperparameter is configured to indicate a number of one or more decision trees to be included in the random forest based machine learning model, wherein the min_samples_split hyperparameter is configured to set a pre-determined number of one or more data points required in a node before the one or more data points split during a tree-building process, wherein the max_features hyperparameter is configured to determine an optimum number of the one or more payment information features when the optimum split of the one or more payment information features at each node in the random forest based machine learning model, wherein the min_samples_leaf is configured to indicate the pre-determined number of one or more data points required to generate a leaf node during the tree-building process; and generating, by the one or more hardware processors, the one or more confidence scores for the one or more optimum information tokens, based on the trained machine learning model” (claims 7 and 16: further narrowing the recited abstract idea, except “obtaining” step is insignificant extra solution activity);
“validating, by the one or more hardware processors, the machine learning model based on the one or more validation datasets, wherein validating the machine learning model comprises: determining, by the one or more hardware processors, whether one or more metric scores attained by the trained machine learning model, exceeds one or more pre-determined threshold values, wherein the one or more metric scores are associated with one or more validation metrics comprising at least one of: precision metric, recall metric, FI-score metric, and confusion metric” (claims 8 and 17: further narrowing the recited abstract idea);
“adjusting, by the one or more hardware processors, the one or more hyperparameters to fine-tune the machine learning model based on one or more results of validation of the machine learning model” (claims 9 and 18: further narrowing the recited abstract idea); and
“re-training the machine learning model over the plurality of time intervals comprises: receiving, by the one or more hardware processors, the one or more training data; adding, by the one or more hardware processors, the one or more training data with the one or more training datasets to generate one or more updated training datasets; re-training, by the one or more hardware processors, the machine learning model to correlate the one or more feature vectors associated with the one or more payment information features, with at least one of: the one or more payment amounts and the one or more payment identifiers, wherein the one or more confidence scores are generated based on re-training the machine learning model; and executing, by the one or more hardware processors, the re-trained machine learning model in a payment information determining subsystem to determine the one or more first payment information comprising at least one of: the one or more payment amounts and the one or more payment identifiers within at least one of: the one or more electronic mails and the one or more electronic documents” (claims 10 and 19: further narrowing the recited abstract idea, except “receiving” step is insignificant extra solution activity).
Conclusion of Dependent Claims Analysis: Dependent claims 2-3, 5-10, 12, and 14-19, do not correct the deficiencies of independent claims 1 and 11 and they are, thus, rejected on the same basis.
Conclusion of the 35 USC § 101 Analysis: Therefore, claims 1-3, 5-12, and 14-20, are rejected as directed to an abstract idea without “significantly more” under 35 USC § 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Buskirk (US 6,424,997 B1) discloses: “A machine learning based electronic mail system. A classifier and action selection module analyzes the incoming message and classifies the messages with associated confidence levels, which may include analyzing the electronic message by tokenization of the text, morphological analysis of the text, and other well-known processes. The classifier and action selection module then determines the appropriate action or actions to effect on the message.”
Kushmerick, Nicholas, et al. "Activity-centric email: A machine learning approach." AAAI. Vol. 21. No. 2. 2006.
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/VIRPI H KANERVO/ Primary Examiner, Art Unit 3691
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