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 1-20 are presented for examination. Examiner has established objection to claims 16 and 19; § 101 rejection for claims 1-20; and grounds of § 103 rejection for claims 1-20 in the instant Office action.
Claim Objections
Claim 16 is objected to because of the following informality:
16. The machine-learning based (ML-based) computing method of claim 11, further comprising a training subsystem configured to train the machine learning model, wherein in training the machine learning model, the training subsystem is configured to:
Since independent claim 11 is a system-claim, dependent claim 16 should also be a system-claim instead of method-claim. Applicant could amend claim 16 to recite:
16. The machine-learning based (ML-based) computing system of claim 11, further comprising a training subsystem configured to train the machine learning model, wherein in training the machine learning model, the training subsystem is configured to:
Claim 19 is objected to because of the following informality:
19. The machine-learning based (ML-based) computing system of claim 10, wherein the training sub-system is further configured to re-train the machine teaming model over a plurality of time intervals based on one or more training data, wherein in re-training the machine learning model over the plurality of time intervals , the training system is configured to:
Since dependent claim 19 is a system-claim and dependent claim 10 is a method-claim, it appears by comparing the claim-sets that dependent system claim 19 should depend on dependent system claim 16. Applicant could amend claim 19 to recite:
19. The machine-learning based (ML-based) computing system of claim [[10]] 16, wherein the training sub-system is further configured to re-train the machine teaming model over a plurality of time intervals based on one or more training data, wherein in re-training the machine learning model over the plurality of time intervals , the training system is configured to:
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 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 1-10 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 11-19 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 1-20 are statutory under 35 USC § 101 in view of step 1 of the test.
Step 2A of the Test:
Prong 1: Claims 1-20, 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);
"determining 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 extracted from at least one of: the one or more electronic mails and the one or more electronic documents attached in the one or electronic mails, 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); and
"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 a machine learning model" (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).
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 2-10 depend on independent claim 1; and dependent claims 12-19 depend on independent claim 11. The elements in dependent claims 2-10 and 12-19, 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);
"determining, by the machine learning model. 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, comprises: generating, by the one or more hardware processors. 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, 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; and 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 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" (claims 4 and 13: 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, by: 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, wherein the machine teaming model comprises a random forest based machine learning model; 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; 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, based on one or more hyperparameters, wherein the one or more hyperparameters comprise at least one of: max_depth, class_weight, n_estimators, min_samples_split, max_features, and min_samples_leaf, 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 hyperpararmeter 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, by the one or more hardware processors, the machine teaming model over a plurality of time intervals based on one or more training data, wherein 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 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; 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 2-10 and 12-19 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 1-20 are rejected as directed to an abstract idea without “significantly more” under 35 USC § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in § 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Lindeman (US 10,748,134 B2) in view of Fenichel (US 11,966,921 B2).
As to independent claims 1, 11, and 20
Lindeman shows:
one or more hardware processors (Lindeman: col. 8, lines 1-10);
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 (Lindeman: col. 8, lines 1-10) to execute the following steps:
receiving one or more data from one or more databases, 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 (Lindeman: col. 5, lines 59-67; and col. 6, lines 1-2);
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 (Lindeman: col. 5, lines 59-67; col. 6, lines 1-2; and col. 7, lines 4-8); and
determining 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 extracted from at least one of: the one or more electronic mails and the one or more electronic documents attached in the one or electronic mails, 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 (Lindeman: col. 5, lines 59-67; col. 6, lines 1-2 and 10-19; and col. 7, lines 4-8).
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 (Lindeman: col. 7, lines 4-8);
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 (Lindeman: col. 6, lines 1-2 and 10-19; and col. 7, lines 4-14);
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 on a user interface associated with one or more electronic devices (Lindeman: col. 5, lines 59-67; and col. 6, lines 1-2 and 10-19).
Lindeman does not show:
determining the one or more first payment information for the one or more optimum information tokens by a machine learning model.
Fenichel shows:
determining the one or more first payment information for the one or more optimum information tokens by a machine learning model (Fenichel: col. 11, lines 11-59).
Motivation to combine Lindeman and Fenichel:
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method, the system, and the non-transitory computer readable medium of Lindeman by determining the one or more first payment information for the one or more optimum information tokens by a machine learning model of Fenichel in order to improve authorizing systems using tokes (Fenichel: col. 1, lines 55-57).
As to claims 2 and 12, Lindeman in view of Fenichel shows all the elements of claims 1 and 11. Lindeman also shows converting 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 (Lindeman: col. 15, lines 9-16); and transforming the one or more text formats into the one or more information tokens, based on a tokenization process (Lindeman: col. 5. Lines 46-58).
As to claim 3, Lindeman in view of Fenichel shows all the elements of claim 1. Lindeman also shows that 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 (Lindeman: col. 6, lines 10-12).
As to claims 4 and 13, Lindeman in view of Fenichel shows all the elements of claims 1 and 11. Lindeman does not show 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, 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; 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. Fenichel shows 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, 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 (Fenichel: col. 11, lines 22-34); 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 (Fenichel: col. 11, lines 22-34). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and the system of Lindeman by 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, 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; 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 of Fenichel in order to improve authorizing systems using tokes (Fenichel: col. 1, lines 55-57).
As to claims 5 and 14, Lindeman in view of Fenichel shows all the elements of claims 4 and 13. Lindeman does not show 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. Fenichel shows 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 (Fenichel: col. 11, lines 22-34). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and the system of Lindeman by 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 of Fenichel in order to improve authorizing systems using tokes (Fenichel: col. 1, lines 55-57).
As to claims 6 and 15, Lindeman in view of Fenichel shows all the elements of claims 4 and 13. Lindeman does not show classifying 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 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. Fenichel shows classifying 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 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 (Fenichel: col. 11, lines 22-34). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and the system of Lindeman by classifying 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 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 of Fenichel in order to improve authorizing systems using tokes (Fenichel: col. 1, lines 55-57).
As to claims 7 and 16, Lindeman in view of Fenichel shows all the elements of claims 1 and 11. Lindeman does not show training the machine learning model, by: obtaining 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 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, wherein the machine teaming model comprises a random forest based machine learning model; 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 the one or more non-payment information; segmenting the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets; training 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, based on one or more hyperparameters, wherein the one or more hyperparameters comprise at least one of: max_depth, class_weight, n_estimators, min_samples_split, max_features, and min_samples_leaf, 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 hyperpararmeter 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 the one or more confidence scores for the one or more optimum information tokens, based on the trained machine learning model. Fenichel shows training the machine learning model, by: obtaining 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 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, wherein the machine teaming model comprises a random forest based machine learning model; 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 the one or more non-payment information; segmenting the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets; training 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, based on one or more hyperparameters, wherein the one or more hyperparameters comprise at least one of: max_depth, class_weight, n_estimators, min_samples_split, max_features, and min_samples_leaf, 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 hyperpararmeter 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