Patent Application 17661300 - FORECASTING PERIODIC INTER-ENTITY EXPOSURE FOR

Title: FORECASTING PERIODIC INTER-ENTITY EXPOSURE FOR PROPHYLACTIC MITIGATION

Application Information

Invention Title: FORECASTING PERIODIC INTER-ENTITY EXPOSURE FOR PROPHYLACTIC MITIGATION
Application Number: 17661300
Submission Date: 2025-05-13T00:00:00.000Z
Effective Filing Date: 2022-04-29T00:00:00.000Z
Filing Date: 2022-04-29T00:00:00.000Z
National Class: 705
National Sub-Class: 007280
Examiner Employee Number: 93084
Art Unit: 3694
Tech Center: 3600

Rejection Summary

102 Rejections: 0
103 Rejections: 1

Cited Patents

No patents were cited in this rejection.

Office Action Text

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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.

The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.

Claims 1, 3-7, 9-13, 15-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
According to MPEP 2173.05(a)(I), “The meaning of every term used in a claim should be apparent from the prior art or from the specification and drawings at the time the application is filed. Claim language may not be "ambiguous, vague, incoherent, opaque, or otherwise unclear in describing and defining the claimed invention." In re Packard, 751 F.3d 1307, 1311, 110 USPQ2d 1785, 1787 (Fed. Cir. 2014). Applicants need not confine themselves to the terminology used in the prior art, but are required to make clear and precise the terms that are used to define the invention whereby the metes and bounds of the claimed invention can be ascertained” and according to MPEP 2173.04: “a genus claim that could be interpreted in such a way that it is not clear which species are covered would be indefinite (e.g., because there is more than one reasonable interpretation of what species are included in the claim.”
Independent claims 1, 11, and 19 recite “exposure of the user entity to third party envelopment.” The Specification filed 29 April 2022 provides examples of “third party envelopment”, but does not provide a definition for the term.
¶[0002] of the Specification states that entities are subject to “third party envelopments such as monitoring, auditing, and enforced reporting and/or payment requirements”
¶[0111] of the Specification states that periodic payments are made for “envelopments such as taxes”.
¶[0125] of the Specification states that “taxation, auditing, garnishment, and seizure of funds can be described as envelopment.”
¶[0106] of the Specification states that “In the non-limiting example of FIG. 7, exposure to tax liability is mitigated.”
¶[0106] of the Specification explains that third parties could include “one or more government entities or departments, such as the Internal Revenue Service of the United States, state treasury departments, municipal treasury departments, county treasury departments” and that “all or any of which may levy taxes, tariffs, tolls, fees and/or fines”.
¶[0108] of the Specification states that a user may remiss with regard to withholding for income tax purposes, and a liability therefor may be accruing over time. In some instances, individuals and small business are subject to hefty tax bills and related fees due to insufficient quarterly withholding.
It is unclear if the scope of the term “third-party envelopment” is limited to government entities or whether it extends to private entities, or whether the scope also includes voluntary disclosures to regulatory bodies, private contractional obligations (with penalties such as late fees), general regulatory burdens, notices of debt collection, or civil judgments. What the term “third-party envelopment” could mean to a person of skill in the art are so numerous that one would not be reasonably apprised of the scope of the invention; and so, the metes and bounds of the claim cannot be determined. According to MPEP 2173.02(I) “, if the language of a claim, given its broadest reasonable interpretation, is such that a person of ordinary skill in the relevant art would read it with more than one reasonable interpretation, then a rejection under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph is appropriate.”
Thus, claims 1, 11 and 19 are rejected as being indefinite under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. Each of claims 3-7, 9-10, 12-13, 15-18, and 20 are dependent directly or indirectly on claim 1, 11, or 19 and are rejected for the same reason.
In the interest of compact prosecution and for purposes of examination, “envelopment” is interpreted to mean taxes (see Specification at ¶[0011]: “envelopments such as taxes”).

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, 3-7, 9-13, 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recite(s):
performs steps comprising, for each user entity of multiple user entities:
storing event records associated with the user entity, each of the event records representing at least one of a quantized output event and a quantized input event, each event record comprising a respective timestamp indicating a time of each represented quantized output event and each represented quantized input event;
discharging a corresponding respective output quantity for each quantized output event from a first resource of the user entity;
fetching a corresponding respective input quantity for each quantized input event to the first resource or a second resource of the user entity;
training, via machine learning and using a set of training data, an algorithm to determine an exposure metric representing exposure of the user entity to third party envelopment at a time interval subsequent to the timestamp of the event records, the third party at least periodically executing a determinative protocol upon at least some of the user entities to determine fulfillment or nonfulfillment of prescribed actions by said at least some user entities, the training including:
iteratively predicting the exposure metric for the event records associated with the user entity, the predicting being based on at least one output category with which output event records of multiple user-entities are associated; testing and comparing the exposure metric predicted during each iteration against a target variable; and indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain entity data of the multiple user entities are necessary to improve predictability of the target variable;
deploying the trained algorithm to generate an exposure metric representing exposure of the user entity to third party envelopment for the user entity using one or more output categories, and based thereon determining whether the exposure metric exceeds or subceeds a threshold of fulfillment or nonfulfillment of prescribed actions respective to the user entity; and
triggering, upon the exposure metric exceeding or subceeding a threshold of fulfillment or nonfulfillment of the prescribed actions respective to the user entity, an alert for display […] to a user […] of the user entity of forecasted third party envelopment, the alert directing the user entity to a mitigation service to mitigate the third party envelopment when the exposure metric exceeds the threshold.

The concept falls under the grouping of abstract ideas of fundamental economic principles or practices including mitigating risk (see MPEP 2106.04(a)(2) subsection II.A.) at least because it recites “determin[ing] an exposure metric representing exposure of the user entity to third party envelopment” and “directing the user entity to a mitigation service to mitigate the third party envelopment when the exposure metric exceeds the threshold”. Thus, the claim recites an abstract idea (Eligibility Step 2A: YES).
This judicial exception is not integrated into a practical application because the additional elements in the claims include: a computing system including one or more processor and at least one of a memory device and a non-transitory storage device, wherein said one or more processor executes computer-readable instructions; and a network connection operatively connecting user devices to the computing system and sen[ding] across the network connection to a user device.
Applicant’s Specification filed on 29 April 2022 states that ‘the computer program instructions may be provided to a processor of a general purpose computer” (Specification at ¶[0039]) and “ The network 258 may include any internal or external network, networks, sub-network, and combinations of such operable to implement communications between various computing components […]” (Specification at ¶[0074]).
Thus, the additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the abstract idea using generic computer and computer networking components or amount to merely using a computer as a tool to perform the abstract idea. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements, individually and in combination, that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components or merely using a computer as a tool to perform the abstract ideas amount to no more than mere instructions to apply the exception using generic computer and computer network components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Thus, the claim is not patent-eligible.
Independent claims 11 and 19 recite substantially the same abstract idea but use the words increment/decrement instead of fetch/discharge and are rejected for the same reason.
Claim 3 recites an accounting or business rule regarding transfer of resources which is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 4 recites forecasting a quantitative impact of the third party envelopment on resources of the user entity which is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 5 adds that a determination is made for time intervals. This is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 6 specifies when fulfillment is required. This is a rule to follow and is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 7 adds that a report is required of the user entity periodically. This is a rule that a user or entity follows and is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 9 specifies which records are used to make the determination. This is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 10 specifies which records are used to make the determination. This is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 12 specifies which records are used to make the determination. This is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 13. specifies which records are used to make the determination. This is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 15 recites an accounting or business rule regarding transfer of resources which is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 16. recites forecasting a quantitative impact of the third party envelopment on resources of the user entity which is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 17 specifies when fulfillment is required. This is a rule to follow and is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 18 adds that a report is required of the user entity periodically. This is a rule a user/entity follows and is part of the abstract idea, includes no additional elements and does not provide significantly more.
Claim 20 specifies which records are used to make the determination. This is part of the abstract idea, includes no additional elements and does not provide significantly more.
Accordingly, Claims 1, 3-7, 9-13, 15-20 are rejected under 35 U.S.C. § 101.

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.

The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.

Claim(s) 1, 3-7, 9-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over GONZALEZ (US 20200394721 A1 to GONZALEZ; Richard C. et al.) in view of SKALSKI (US 20240232889 A1 to Skalski; Piotr et al.) in further view of BISWAS (US 11062327 B2 to Biswas; Kamal et al.).
Regarding claim(s) 1,
GONZALEZ discloses:
An alert generating system using event monitoring to mitigate exposure of at least one resource of each of multiple user entities to third party envelopment, the system comprising (GONZALEZ: Abstract: a product and system that eases the burden of analyzing income, assessing tax liability, creating tax set-asides for independent contractor tax payments, and visually conveying the information to a user; ¶[0023]: the analysis of the tax situation is performed continuously; the tax analysis is performed contemporaneously with each new deposit of taxable income; a continuous cycle of projections and estimations that better ensure appropriate tax set-asides are collected and paid; ¶[0030]: payments received to a plurality of user accounts; ¶[0033]: description applies to each customer of the tax calculation and computer; ¶[0077]: the alert text 424 conveys to the user, in a macro-sense, where the user stands from a tax perspective; ¶[0020]: Failure to make periodic estimated tax payments can result in substantial penalties):
a computing system including one or more processor and at least one of a memory device and a non-transitory storage device, wherein said one or more processor executes computer-readable instructions (GONZALEZ: ¶[0087]: compute, processor, memory, programs); and
a network connection operatively connecting user devices to the computing system (GONZALEZ: ¶[0025]: user computing device communicating through a digital communication network),
wherein, upon execution of the computer-readable instructions, the computing system performs steps comprising, for each user entity of multiple user entities (GONZALEZ: ¶[0033]: description applies to each customer of the tax calculation and computer):
storing event records associated with the user entity, each of the event records representing at least one of a quantized output event and a quantized input event, each event record comprising a respective timestamp indicating a time of each represented quantized output event and each represented quantized input event [(bold emphasis added — see discussion of SKALSKI, below, for the teaching of the limitations not in bold)] (GONZALEZ: figure 5 and ¶[072]: “Within the data area 406 are shown various deposits as identified by the system, including a designation in this example that there […] new deposits in that categorization”; figure 5:item 406, “new Deposits” label and indication; listed date and amounts of individual deposits (e.g. “12-31-2018” for USAA FUNDS TRASNFER CR”); GONZALEZ: ¶[0033]: customer deposit table 226 that stores information regarding deposits in the user account 200; ¶[0033]: customer payments table 232 that stores information about periodic tax payments made by the customers; ¶[0029]: computer 112 is provided access to the accounts 108 and 110 of the user 102 within the bank 106; ¶[0041]: the software routines periodically (e.g., daily, multiple times each day) polls the accounts of user 102 looking for new transactions of the primary account.; ¶[0041]: stored procedures traverse the account and gather all transaction data; ¶[0050]: for each new deposit designated as taxable income the assessment module 216 may calculate a year-to-date income value by summing the new deposit and all previous deposits indicated as taxable income in the current tax year; ¶[0066]: Each deposit that is made from an outside source is recorded to present to the worker as a source of income; ¶[0078]: periods of any suitable length; ¶[0079]: The system creates the payment bar 410 based on the sum of the previous tax payments for the current tax year and the value in the user's tax set-aside account);
discharging a corresponding respective output quantity for each quantized output event from a first resource of the user entity (GONZALEZ: ¶[0028]: Computer systems of the bank 106 may make electronic funds transfers directly, or through intermediaries, to the IRS 116 to facilitate periodic estimated tax payments; ¶[0029]: the tax calculation computer 112 may work with the bank 106 to electronically transfer the funds from the tax set-aside account 110 to the IRS 116 either directly or through an intermediary; ¶[0042]: integration enables the system to move money on behalf of the worker for set asides and eventual payments to the revenue collection agency; ¶[0042]: ability to analyze all of the user's 102 transactions.);
fetching a corresponding respective input quantity for each quantized input event to the first resource or a second resource of the user entity (GONZALEZ: ¶[0029]: the tax calculation computer 112 may interact with the bank 106 to cause the recommended amount to be transferred from the primary account 108 to the tax set-aside account 110.; ¶[0042]: integration enables the system to move money on behalf of the worker for set asides; ¶[0033]: customer set-aside table 230 that stores information about tax set-asides of each customer; customer payments table 232 that stores information about periodic tax payments made by the customers);
[…]
and triggering, upon the exposure metric exceeding or subceeding a threshold of fulfillment or nonfulfillment of the prescribed actions respective to the user entity, an alert for display sent across the network connection to a user device of the user entity of forecasted third party envelopment (GONZALEZ: figure 4 and ¶[0077]: Additionally, the alert text 424 conveys to the user, in a macro-sense, where the user stands from a tax perspective. In the example status screen 400, the user is “PLAYING CATCH UP.” However, many other alert text 424 may be used to quickly and efficiently convey to the user the status of the tax considerations. For example, the alert text 424 may include: “Surplus,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than the projected tax burden for the current period, and that suggested values for tax set-aside (or portions thereof) may be optional; “Equal,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is equal to the projected tax burden for the current period; “OK” or “DOING GREAT” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is less than 5% behind the projected tax burden for the current period; “Warning,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than 5% but less than 25% behind the projected tax burden for the current period; and “Severe” or “Playing Catch Up,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than 25% behind the projected tax burden for the current period. Other wording for alert text 424, and other thresholds, may be used.; ¶[0080]: if the user borrows from the tax set-aside account for an emergency, the overall status may change (e.g., from “Equal” to “Warning”). The change in status in the alert text 424, and corresponding changes to the payment bar 410 and remaining bar 412, changes the parameters of the tax set-aside recommendations in an effort to get the user caught up and back on track. When the user receives a new income deposit, the recommended tax set-aside will be higher, thus having the user pay back the tax set-aside account.).
the alert directing the user entity to a mitigation service to mitigate the third party envelopment (GONZALEZ: (¶[0077]: the alert text; ¶[0077]: Warning,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than 5% behind the projected tax burden for the current period; and “Severe” or “Playing Catch Up,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than 25% behind the projected tax burden for the current period. Other wording for alert text 424, and other thresholds, may be used; ¶[0039]: user computing device 104 and the web services interface used to access a third party service that acts as an intermediary to the primary account 108 (FIG. 1) at the bank.; ¶[0038]: In some example systems, access to the user's accounts is provided by way of a third party vendor, such as the services provided by Plaid Inc. (www.plaid.com) of San Francisco); ¶[0080]: recommended tax set-aside; ¶[0029]: if the period for the periodic estimated tax payment is coming to a close, the tax calculation computer 112 may work with the bank 106 to electronically transfer the funds from the tax set-aside account 110 to the IRS through an intermediary; ¶[0028]: Computer systems of the bank 106 may make electronic funds transfers through intermediaries, to the IRS 116 to facilitate periodic estimated tax payments);
when the exposure metric exceeds the threshold (GONZALEZ: ¶[0077]: Warning,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than 5% behind the projected tax burden for the current period; and “Severe” or “Playing Catch Up,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than 25% behind the projected tax burden for the current period. Other wording for alert text 424, and other thresholds, may be used).

GONZALEZ does not expressly disclose the following limitations, which SKALSKI however, teaches:
each event record comprising a respective timestamp indicating a time of each represented quantized output event and each represented quantized input event (SKALSKI: ¶[0153]: FIG. 3B shows how transaction data 330 for a particular transaction may be stored in numeric form for processing by one or more machine learning models. For example, in FIG. 3B, transaction data has at least fields: transaction amount, timestamp (e.g., as a Unix epoch), transaction type (e.g., card payment or direct debit), product description or identifier (i.e., relating to items being purchased), merchant identifier, issuing bank identifier, a set of characters (e.g., Unicode characters within a field of predefined character length), country identifier etc.; ¶[0152]: FIGS. 3A and 3B show examples of transaction data that may be processed by a machine learning system . FIG. 3A shows how transaction data may comprise a set of time-ordered records 300, where each record has a timestamp and comprises a plurality of transaction fields. In certain cases, transaction data may be grouped and/or filtered based on the timestamp. For example, FIG. 3A shows a partition of transaction data into current transaction data 310 that is associated with a current transaction and “older” or historical transaction data 320 that is within a predefined time range of the current transaction. The time range may be set as a hyperparameter of any machine learning system; [0112] Exemplary embodiments may be applied to a wide variety of digital transactions, including, but not limited to, card payments, so-called “wire” transfers, peer-to-peer payments, Bankers' Automated Clearing System (BACS) payments, and Automated Clearing House (ACH) payments. The output of the machine learning system may be used to prevent a wide variety of fraudulent and criminal behaviour such as card fraud, application fraud, payment fraud, merchant fraud, gaming fraud and money laundering.)
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of GONZALEZ, which discloses systems and methods of analyzing user’s accounts and transactions(¶[0042]) and managing tax liabilities (GONZALEZ ¶[0018]) with the technique of SKALSKI, in order to enable advanced analysis of transactions (see SKALSKI ¶[0120]) and to allow time-ordering of records so that transaction data may be grouped or filtered based on the timestamp and use time-ranges for analysis including as a hyperparameter of a machine learning system (SKALSKI ¶[0152]).

GONZALEZ does not expressly disclose the following limitations, which BISWAS however, teaches:
training, via machine learning and using a set of training data, an algorithm (BISWAS: col. 18, ll. 26-33: the regulatory compliance assessment system 520 processes multiple records (e.g., millions of records) to prioritize actions to be taken by the entity to increase or improve an associated risk compliance index score. In an embodiment, one or more machine learning algorithms executed by the machine learning component of the regulatory compliance assessment system 520 to refine the recommendations as data changes over time; BISWAS col. 5, ll. 30-56:, the data monitoring component can receive, retrieve, collect, or download raw regulatory-related data associated with an entity from one or more data source systems and/or one or more user systems including historical data and can be collected on a periodic and iterative basis to capture changes in the data and enable an updated calculation of the risk compliance index score; BISWAS col. 10, ll. 13-19: base line data sources can include a collection of data obtained over a period of time (e.g., multiple years). Older data can be considered to be less relevant. Accordingly, while calculating the corresponding risk score, the age of the data can be taken into account).
to determine an exposure metric representing exposure of the user entity to third party envelopment at a time interval subsequent to the timestamp of the event records (BISWAS: col. 10, ll. 12-18: a BLDS can include a collection of data obtained over a period of time (e.g., multiple years). In an embodiment, older data can be considered to be less relevant. Accordingly, while calculating the corresponding risk score, the age of the data can be taken into account a time-based weight T(0); BISWAS col. 5, ll. 30-45: the regulatory-related data can be collected on a periodic and iterative basis (e.g., once a day, every day) to capture changes in the data and enable an updated calculation of the associated risk compliance index score),
the third party at least periodically executing a determinative protocol upon at least some of the user entities to determine fulfillment or nonfulfillment of prescribed actions by said at least some user entities (BISWAS: col. 6, ll. 3-5: Examples [data sources] include FDA warning letters, court-imposed fines and settlements on industry companies; BISWAS: col. 5, ll. 39-42: the regulatory-related data can be collected on a periodic and iterative basis (e.g., once a day, every day) to capture changes in the data and enable an updated calculation of the associated risk compliance index score; col. 6, ll. 1-6: FDA warning letters, court-imposed fines, reported non-compliance issues),
the training including: iteratively predicting the exposure metric for the event records associated with the user entity (BISWAS: col. 5, ll. 30-42: the data monitoring component 122 can receive, retrieve, collect, or download raw regulatory related data associated with an entity from one or more data source systems and/or one or more user systems 102. The regulatory-related data can include company assessment data (e.g., internal audits, external audits, data associated with questionnaires), historical data ( e.g., audit failures, fines and settlements, contractual obligations, etc.), FDA data ( e.g., FDA 483 classifications), etc. In an embodiment, the regulatory-related data can be collected on a periodic and iterative basis (e.g., once a day, every day) to capture changes in the data and enable an updated calculation of the associated risk compliance index score; col. 11, ll. 20-28: risk compliance index score generator 128 processes multiple data records ( e.g., millions of data objects) processed by the machine learning component 124 to refine the recommendations ( e.g., recommend actions) as data changes over time; col. 13, ll. 15-20: operations of the method 200 can be performed iteratively, such that operations 210-230 can be repeated to generate one or more new or updated risk compliance scores to be output in operation)
the predicting being based on at least one output category with which output event records of multiple user-entities are associated (BISWAS: col. 3, ll. 15-20: the regulatory compliance assessment system collects and analyzes data from multiple data source systems in generating the risk compliance index score; col. 3, ll. 46-50: The risk compliance score of entities in a specific industry segment ( e.g. pharmaceutical industry) can be compared and presented at the industry level risk compliance score; col. 5, ll. 65-67 to col. 6, ll. 1-2: the data monitoring and extraction module 122 collects raw regulator-related data from one or more data sources that are independent of a specific entity 6 (e.g., company) or specific audit and are generally available in the public domain; col. 6, ll. 17-21: machine learning component 124 is configured to analyze the extracted data elements of the collected regulatory- 20 related data to classify the data based on function types, control types, and findings levels; col. 6, ll. 3-8: data includes court-imposed fines and settlements on industry companies);
testing and comparing the exposure metric predicted during each iteration against a target variable (BISWAS: col. 11, ll. 29-32: the regulatory compliance assessment system 120 monitors systems and processes of an entity and data from multiple data sources in real-time to refine their actions in view of potential or identified non-conformances; BISWAS: col. 11, ll. 20-28: risk compliance index score generator 128 processes multiple data records ( e.g., millions of data objects) processed by the machine learning component 124 to refine the recommendations ( e.g., recommend actions) as data changes over time to generate an action plan including multiple prioritized or recommended actions; col. 13, ll. 15-20: operations of the method 200 can be performed iteratively, such that operations 210-230 can be repeated to generate one or more new or updated risk compliance scores to be output in operation.);
and indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain entity data of the multiple user entities are necessary to improve predictability of the target variable (BISWAS: col. 18, ll. 25-44: In an embodiment, the regulatory compliance assessment system 520 processes multiple records (e.g., millions of records) to prioritize actions to be taken by the entity to increase or improve an associated risk compliance index score. In an embodiment, one or more machine learning algorithms executed by the machine learning component of the regulatory compliance assessment system 520 to refine the recommendations as data changes over time. The risk compliance profile including the one or more risk compliance index scores and corresponding prioritized actions can enable an entity to take actions based on their identified business risks. Advantageously, the regulatory compliance assessment system 520 iteratively and repeatedly monitors an entity's systems and information in real-time to identify the recommended actions to be executed by an entity (e.g., identify potential non-conformances and associated actions to assist the entity in establishing conformity) and iteratively refine or update the corresponding risk compliance index score for the entity; col. 11, ll. 29-36: In an embodiment, the regulatory compliance assessment system 120 monitors systems and processes of an entity and data from multiple data sources in real-time to refine their actions in view of potential or identified non-conformances. In an embodiment, information associated with the identified actions can be provided by the compliance prediction module 131 to the machine learning component 124);
deploying the trained algorithm to generate an exposure metric representing exposure of the user entity to third party envelopment for the user entity using one or more output categories (BISWAS: col. 5, ll. 4: 6-8: enable the generation of a risk compliance index score associated with the entity in accordance with the methods described; col. 13, ll. 15-20: operations of the method 200 can be performed iteratively, such that operations 210-230 can be repeated to generate one or more new or updated risk compliance scores to be output in operation),
and based thereon determining whether the exposure metric exceeds or subceeds a threshold of fulfillment or nonfulfillment of prescribed actions respective to the user entity (BISWAS: col. 11, ll. 1-12: the compliance prediction module 131 is configured to generate more actions based on the risk compliance index scores to enable an entity prioritize compliance- related activities in view of the identified business risks. For example, a QA function can have a "Process" control type score of 0.95 and a score of 0.31 in an "Investigation" control type. The QA team now has the ability to prioritize the "Process" work ahead of "Investigation" as the risk related to "Process" is more than the "Investigation". In another example, a Facility function can have a score of 0.57 in a "Technology" control type for the same organization; BISWAS: col. 11, ll. 15-28: In the example above, the QA process had a high risk score due to not having a training SOP in place and having training records that were not current. The system can identify a "Create training SOP" action and an "Update training records" action that can be executed to reduce the QA Process risk score.);
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of GONZALEZ, which discloses systems and methods of managing tax liabilities (GONZALEZ ¶[0018]) and avoiding substantial penalties for non-compliance (GONZALEZ ¶[0020]) with the technique of BISWAS, in order to be able to process more records/data to refine recommendations and update risk scores for noncompliance (BISWAS col. 18, ll. 26-44) and to enable refinement of the risk assessments (BISWAS col. 11, ll. 29-36 ).

Regarding claim(s) 3,
The combination of GONZALEZ, SKALSKI, and BISWAS discloses the system of claim 1.
GONZALEZ further discloses:
further comprising, transferring from at least one of the first resource and second resource a contribution to a resource exempted from said prescribed actions (GONZALEZ: ¶[0005]: embodiments automate creating set-asides for tax payments, giving the user a similar experience to an employee of an established business; ¶[0032]: temporary set-aside module 218 (hereafter set-aside module 218); ¶[0042]: integration enables the system to move money on behalf of the worker for set asides; ¶[0057]:the user may have many deductible business expenses that reduce taxable income, and thus reduce the recommended tax set-aside values. Thus, the example tax calculation computer 112 assists the user 102 in identifying deductible business expenses, and in turn reducing the estimated income values and corresponding estimated taxes.).

Regarding claim(s) 4,
The combination of GONZALEZ, SKALSKI, and BISWAS discloses the system of claim 1:
GONZALEZ further discloses:
wherein determining the exposure metric representing exposure of the user entity to third party envelopment comprises forecasting a quantitative impact of the third party envelopment on resources of the user entity (GONZALEZ: ¶[0050]: The example embodiments then calculate or project an annual tax burden of the projected annual income. That is, for example, using the tax tables 234 the example embodiments calculate an annual tax burden based on the projected annual income. Next, the example embodiments project a remaining tax burden based on previous tax payments for the current tax year and a value in the tax set-aside account (e.g., subtract the previous tax payments for the current tax year and a value in the tax set-aside account from the annual tax burden). Next, the example embodiments calculate an expected future income value (e.g., the difference between the annual income value and the new income value). Thereafter, the example embodiments may calculate an adjusted set-aside percentage based on the remaining tax burden and the expected future income, and recommend a tax set-aside value of the new deposit based on the adjusted set-aside percentage and the new deposit.).

Regarding claim(s) 5,
The combination of GONZALEZ, SKALSKI, and BISWAS discloses the system of claim 1.
GONZALEZ further discloses:
wherein fulfillment or nonfulfillment of the prescribed actions is determined for time intervals (GONZALEZ: ¶[00501]: The granularity of the period used to calculate the average periodic income may be any suitable period, such as calendar quarter, month, day, hour, half-hour, minute, and so on; ¶[0041]: In the example systems and methods, the software routines periodically (e.g., daily, multiple times each day) polls the accounts of user 102 looking for new transactions of the primary account; ¶[0072]: period is tax quarters); ¶[0018]: calculating tax liability and setting aside funds to meet periodic tax payments (e.g., quarterly in the United States, biannually in other countries).

Regarding claim(s) 6,
The combination of GONZALEZ, SKALSKI, and BISWAS discloses the system of claim 5.
GONZALEZ further discloses:
wherein fulfillment of the prescribed actions for any given time interval is required by the third party in a time interval subsequent to the given time interval (GONZALEZ: ¶[0029]: if the period for the periodic estimated tax payment is coming to a close, the tax calculation computer 112 may work with the bank 106 to electronically transfer the funds from the tax set-aside account 110 to the IRS 116 either directly or through an intermediary; ¶[0050]: example embodiments calculate an expected future income value (e.g., the difference between the annual income value and the new income value). Thereafter, the example embodiments may calculate an adjusted set-aside percentage based on the remaining tax burden and the expected future income, and recommend a tax set-aside value of the new deposit based on the adjusted set-aside percentage and the new deposit.).

Regarding claim(s) 7,
The combination of GONZALEZ, SKALSKI, and BISWAS discloses the system of claim 1.
GONZALEZ further discloses:
wherein a report to the third party of fulfillment or nonfulfillment of the prescribed actions is required of the user entity periodically (GONZALEZ: ¶[0002]: U.S. government fails to collect a portion of taxes due because independent contractors and self-employed individuals fail to properly report and manage their tax responsibilities; ¶[0020]: most revenue collection agencies also require periodic estimated tax payments to be made. In the United States, for example, the Internal Revenue Services (IRS) requires tax payers to make quarterly estimated tax payments. Failure to make periodic estimated tax payments can result in substantial penalties, in some countries approaching 50%; ¶[0021]: describes typical process of collecting data regarding income over the quarter, estimating tax, and making the estimated tax payment for the quarter to the IRS. The process continues once each quarter, with the end-of-tax-year reconciliation at tax time; ¶[0023]: The various example embodiments were developed in the context of periodic estimated tax payments in the United States; the many taxing authorities (e.g., cities, counties, states, and countries) expect periodic estimated tax payments to be made, and the various embodiments are applicable to any such taxing authority, including multiple simultaneous taxing authorities. ).

Regarding claim(s) 9,
The combination of GONZALEZ, SKALSKI, and BISWAS teaches the system of claim 1.
GONZALEZ does not expressly disclose the following limitations, which BISWAS however, teaches:
wherein the algorithm utilizes records, specific to the user entity, of fulfillment or nonfulfillment of the prescribed actions in prior time intervals to determine the exposure metric representing exposure of the user entity to third party envelopment in a time interval subsequent to the prior time intervals (BISWAS col. 12, ll. 21-23: collects, from multiple data sources, regulatory-related data associated with an entity; BISWAS: col. 9, ll. 7-12: the data classification module 126 analyzes internal and external audit results including individual procedural Non-Conformance (NC) findings that are accumulated over time; BISWAS col. 5, ll. 30-56: the data monitoring component receives raw regulatory-related data associated with an entity including historical data and can be collected on a periodic and iterative basis to capture changes in the data and enable an updated calculation of the risk compliance index score; BISWAS col. 10, ll. 13-19: base line data sources can include a collection of data obtained over a period of time (e.g., multiple years). Older data can be considered to be less relevant. Accordingly, while calculating the corresponding risk score, the age of the data can be taken into account.).
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of GONZALEZ, which discloses systems and methods of managing tax liabilities (GONZALEZ ¶[0018]) and avoiding substantial penalties for non-compliance (GONZALEZ ¶[0020]) with the technique of BISWAS, in order to be able to process more records/data to refine recommendations and update risk scores for noncompliance (BISWAS col. 18, ll. 26-44) and to enable refinement of the risk assessments (BISWAS col. 11, ll. 29-36 ).

Regarding claim(s) 10,
The combination of GONZALEZ, SKALSKI, and BISWAS teaches the system of claim 1.
GONZALEZ does not expressly disclose the following limitations, which BISWAS however, teaches:
wherein the algorithm utilizes respective records, associated with multiple respective user entities, of fulfillment or nonfulfillment of the prescribed actions (BISWAS col. 5, ll. 65-67 to col. 6, ll. 1-16: In an embodiment, the data monitoring and extraction module 122 collects raw regulator-related data from one or more data sources that are independent of a specific entity (e.g., company) or specific audit and are generally available in the public domain, examples include FDA warning letters, court-imposed fines and settlements on industry companies.; In an embodiment, the collected and extracted regulatory-related data can be stored in a risk and compliance data store)
in prior time intervals to determine the exposure metric representing exposure of the user entity to third party envelopment in a time interval subsequent to the prior time intervals (BISWAS col. 5, ll. 30-56: the data monitoring component receives raw regulatory-related data associated with an entity including historical data and can be collected on a periodic and iterative basis to capture changes in the data and enable an updated calculation of the risk compliance index score).
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of GONZALEZ, which discloses systems and methods of managing tax liabilities (GONZALEZ ¶[0018]) and avoiding substantial penalties for non-compliance (GONZALEZ ¶[0020]) with the technique of BISWAS, in order to be able to process more records/data to refine recommendations and update risk scores for noncompliance (BISWAS col. 18, ll. 26-44) and to enable refinement of the risk assessments (BISWAS col. 11, ll. 29-36 ).

Regarding claim(s) 11 and 19,
GONZALEZ discloses:
An alert generating system (claim 11)[/method (claim 19)] using event monitoring to mitigate exposure of at least one resource of each of multiple user entities to third party envelopment, the system comprising (GONZALEZ: ¶[0023]: the analysis of the tax situation is performed continuously; the tax analysis is performed contemporaneously with each new deposit of taxable income; a continuous cycle of projections and estimations that better ensure appropriate tax set-asides are collected and paid; ¶[0030]: payments received to a plurality of user accounts; ¶[0033]: description applies to each customer of the tax calculation and computer; ¶[0020]: Failure to make periodic estimated tax payments can result in substantial penalties):
a computing system including one or more processor and at least one of a memory device and a non-transitory storage device, wherein said one or more processor executes computer-readable instructions (GONZALEZ: ¶[0087]: compute, processor, memory, programs);
and a network connection operatively connecting user devices to the computing system (GONZALEZ: ¶[0025]: user computing device communicating through a digital communication network),
wherein, upon execution of the computer-readable instructions, the computing system performs steps comprising, for each user entity of multiple user entities (GONZALEZ: ¶[0033]: description applies to each customer of the tax calculation and computer):
storing event records associated with the user entity, each of the event records representing at least one of a quantized output event, for which a first resource of the user entity was decremented (GONZALEZ: ¶[0028]: Computer systems of the bank 106 may make electronic funds transfers directly, or through intermediaries, to the IRS 116 to facilitate periodic estimated tax payments; ¶[0029]: the tax calculation computer 112 may work with the bank 106 to electronically transfer the funds from the tax set-aside account 110 to the IRS 116 either directly or through an intermediary; ¶[0042]: integration enables the system to move money on behalf of the worker for set asides and eventual payments to the revenue collection agency; ¶[0042]: ability to analyze all of the user's 102 transactions.; ¶[0033]: customer payments table 232 that stores information about periodic tax payments made by the customers; ¶[0041]: the software routines periodically (e.g., daily, multiple times each day) polls the accounts of user 102 looking for new transactions of the primary account.; ¶[0041]: stored procedures traverse the account and gather all transaction data; ¶[0079]: The system creates the payment bar 410 based on the sum of the previous tax payments for the current tax year and the value in the user's tax set-aside account),
and a quantized input event, for which the first resource or a second resource of the user entity was incremented, each event record comprising a respective timestamp indicating a time of each represented quantized input event and each represented quantized output event [(bold emphasis added — see discussion of SKALSKI, below, for the teaching of the limitations not in bold)] (GONZALEZ: ¶[0033]: customer deposit table 226 that stores information regarding deposits in the user account 200; ¶[0029]: computer 112 is provided access to the accounts 108 and 110 of the user 102 within the bank 106; ¶[0041]: the software routines periodically (e.g., daily, multiple times each day) polls the accounts of user 102 looking for new transactions of the primary account.; ¶[0041]: stored procedures traverse the account and gather all transaction data; ¶[0050]: for each new deposit designated as taxable income the assessment module 216 may calculate a year-to-date income value by summing the new deposit and all previous deposits indicated as taxable income in the current tax year; ¶[0066]: Each deposit that is made from an outside source is recorded to present to the worker as a source of income; ¶[0078]: periods of any suitable length);
[…]
and triggering, upon the exposure metric exceeding or subceeding a threshold of fulfillment or nonfulfillment of the prescribed actions respective to the user entity, an alert for display sent across the network connection to a user device of the user entity of forecasted third party envelopment (GONZALEZ: figure 4 and ¶[0077]: Additionally, the alert text 424 conveys to the user, in a macro-sense, where the user stands from a tax perspective. In the example status screen 400, the user is “PLAYING CATCH UP.” However, many other alert text 424 may be used to quickly and efficiently convey to the user the status of the tax considerations. For example, the alert text 424 may include: “Surplus,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than the projected tax burden for the current period, and that suggested values for tax set-aside (or portions thereof) may be optional; “Equal,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is equal to the projected tax burden for the current period; “OK” or “DOING GREAT” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is less than 5% behind the projected tax burden for the current period; “Warning,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than 5% but less than 25% behind the projected tax burden for the current period; and “Severe” or “Playing Catch Up,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than 25% behind the projected tax burden for the current period. Other wording for alert text 424, and other thresholds, may be used.; ¶[0080]: if the user borrows from the tax set-aside account for an emergency, the overall status may change (e.g., from “Equal” to “Warning”). The change in status in the alert text 424, and corresponding changes to the payment bar 410 and remaining bar 412, changes the parameters of the tax set-aside recommendations in an effort to get the user caught up and back on track. When the user receives a new income deposit, the recommended tax set-aside will be higher, thus having the user pay back the tax set-aside account.).
wherein when the exposure metric exceeds the threshold (GONZALEZ: ¶[0077]: Warning,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than 5% behind the projected tax burden for the current period; and “Severe” or “Playing Catch Up,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than 25% behind the projected tax burden for the current period. Other wording for alert text 424, and other thresholds, may be used),
the alert directs the user entity to a mitigation service made available by a service entity to mitigate the third party envelopment (GONZALEZ: (¶[0077]: the alert text; ¶[0077]: Warning,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than 5% behind the projected tax burden for the current period; and “Severe” or “Playing Catch Up,” meaning the total of the user's estimated periodic tax payments and value within the user's tax set-aside account is more than 25% behind the projected tax burden for the current period. Other wording for alert text 424, and other thresholds, may be used; ¶[0039]: user computing device 104 and the web services interface used to access a third party service that acts as an intermediary to the primary account 108 (FIG. 1) at the bank.; ¶[0038]: In some example systems, access to the user's accounts is provided by way of a third party vendor, such as the services provided by Plaid Inc. (www.plaid.com) of San Francisco); ¶[0080]: recommended tax set-aside; ¶[0029]: if the period for the periodic estimated tax payment is coming to a close, the tax calculation computer 112 may work with the bank 106 to electronically transfer the funds from the tax set-aside account 110 to the IRS through an intermediary; ¶[0028]: Computer systems of the bank 106 may make electronic funds transfers through intermediaries, to the IRS 116 to facilitate periodic estimated tax payments).

GONZALEZ does not expressly disclose the following limitations, which SKALSKI however, teaches:
each event record comprising a respective timestamp indicating a time of each represented quantized input event and each represented quantized output event (SKALSKI: ¶[0153]: FIG. 3B shows how transaction data 330 for a particular transaction may be stored in numeric form for processing by one or more machine learning models. For example, in FIG. 3B, transaction data has at least fields: transaction amount, timestamp (e.g., as a Unix epoch), transaction type (e.g., card payment or direct debit), product description or identifier (i.e., relating to items being purchased), merchant identifier, issuing bank identifier, a set of characters (e.g., Unicode characters within a field of predefined character length), country identifier etc.; ¶[0152]: FIGS. 3A and 3B show examples of transaction data that may be processed by a machine learning system . FIG. 3A shows how transaction data may comprise a set of time-ordered records 300, where each record has a timestamp and comprises a plurality of transaction fields. In certain cases, transaction data may be grouped and/or filtered based on the timestamp. For example, FIG. 3A shows a partition of transaction data into current transaction data 310 that is associated with a current transaction and “older” or historical transaction data 320 that is within a predefined time range of the current transaction. The time range may be set as a hyperparameter of any machine learning system; [0112] Exemplary embodiments may be applied to a wide variety of digital transactions, including, but not limited to, card payments, so-called “wire” transfers, peer-to-peer payments, Bankers' Automated Clearing System (BACS) payments, and Automated Clearing House (ACH) payments. The output of the machine learning system may be used to prevent a wide variety of fraudulent and criminal behaviour such as card fraud, application fraud, payment fraud, merchant fraud, gaming fraud and money laundering.)
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of GONZALEZ, which discloses systems and methods of analyzing user’s accounts and transactions (¶[0042]) and managing tax liabilities (GONZALEZ ¶[0018]) with the technique of SKALSKI, in order to enable advanced analysis of transactions (see SKALSKI ¶[0120]) and to allow time-ordering of records so that transaction data may be grouped or filtered based on the timestamp and use time-ranges for analysis including as a hyperparameter of a machine learning system (SKALSKI ¶[0152]).

GONZALEZ does not expressly disclose the following limitations, which BISWAS however, teaches:
training, via machine learning and using a set of training data, an algorithm (BISWAS: col. 18, ll. 26-33: the regulatory compliance assessment system 520 processes multiple records (e.g., millions of records) to prioritize actions to be taken by the entity to increase or improve an associated risk compliance index score. In an embodiment, one or more machine learning algorithms executed by the machine learning component of the regulatory compliance assessment system 520 to refine the recommendations as data changes over time; BISWAS col. 5, ll. 30-56:, the data monitoring component can receive, retrieve, collect, or download raw regulatory-related data associated with an entity from one or more data source systems and/or one or more user systems including historical data and can be collected on a periodic and iterative basis to capture changes in the data and enable an updated calculation of the risk compliance index score; BISWAS col. 10, ll. 13-19: base line data sources can include a collection of data obtained over a period of time (e.g., multiple years). Older data can be considered to be less relevant. Accordingly, while calculating the corresponding risk score, the age of the data can be taken into account)
to determine an exposure metric representing exposure of the user entity to third party envelopment at a time interval subsequent to the timestamp of the event records (BISWAS: col. 10, ll. 12-18: a BLDS can include a collection of data obtained over a period of time (e.g., multiple years). In an embodiment, older data can be considered to be less relevant. Accordingly, while calculating the corresponding risk score, the age of the data can be taken into account a time-based weight T(0); BISWAS col. 5, ll. 30-45: the regulatory-related data can be collected on a periodic and iterative basis (e.g., once a day, every day) to capture changes in the data and enable an updated calculation of the associated risk compliance index score),
the third party at least periodically executing a determinative protocol upon at least some of the user entities to determine fulfillment or nonfulfillment of prescribed actions by said at least some user entities (BISWAS: col. 6, ll. 3-5: Examples [data sources] include FDA warning letters, court-imposed fines and settlements on industry companies; BISWAS: col. 5, ll. 39-42: the regulatory-related data can be collected on a periodic and iterative basis (e.g., once a day, every day) to capture changes in the data and enable an updated calculation of the associated risk compliance index score; col. 6, ll. 1-6: FDA warning letters, court-imposed fines, reported non-compliance issues),
the training including: iteratively predicting the exposure metric for the event records associated with the user entity (BISWAS: col. 5, ll. 30-42: the data monitoring component 122 can receive, retrieve, collect, or download raw regulatory related data associated with an entity from one or more data source systems and/or one or more user systems 102. The regulatory-related data can include company assessment data (e.g., internal audits, external audits, data associated with questionnaires), historical data ( e.g., audit failures, fines and settlements, contractual obligations, etc.), FDA data ( e.g., FDA 483 classifications), etc. In an embodiment, the regulatory-related data can be collected on a periodic and iterative basis (e.g., once a day, every day) to capture changes in the data and enable an updated calculation of the associated risk compliance index score; col. 11, ll. 20-28: risk compliance index score generator 128 processes multiple data records ( e.g., millions of data objects) processed by the machine learning component 124 to refine the recommendations ( e.g., recommend actions) as data changes over time; col. 13, ll. 15-20: operations of the method 200 can be performed iteratively, such that operations 210-230 can be repeated to generate one or more new or updated risk compliance scores to be output in operation),
the predicting being based on at least one output category with which output event records of multiple user-entities are associated (BISWAS: col. 3, ll. 15-20: the regulatory compliance assessment system collects and analyzes data from multiple data source systems in generating the risk compliance index score; col. 3, ll. 46-50: The risk compliance score of entities in a specific industry segment ( e.g. pharmaceutical industry) can be compared and presented at the industry level risk compliance score; col. 5, ll. 65-67 to col. 6, ll. 1-2: the data monitoring and extraction module 122 collects raw regulator-related data from one or more data sources that are independent of a specific entity 6 (e.g., company) or specific audit and are generally available in the public domain; col. 6, ll. 17-21: machine learning component 124 is configured to analyze the extracted data elements of the collected regulatory- 20 related data to classify the data based on function types, control types, and findings levels; col. 6, ll. 3-8: data includes court-imposed fines and settlements on industry companies);
testing and comparing the exposure metric predicted during each iteration against a target variable (BISWAS: col. 11, ll. 29-32: the regulatory compliance assessment system 120 monitors systems and processes of an entity and data from multiple data sources in real-time to refine their actions in view of potential or identified non-conformances; BISWAS: col. 11, ll. 20-28: risk compliance index score generator 128 processes multiple data records ( e.g., millions of data objects) processed by the machine learning component 124 to refine the recommendations ( e.g., recommend actions) as data changes over time to generate an action plan including multiple prioritized or recommended actions; col. 13, ll. 15-20: operations of the method 200 can be performed iteratively, such that operations 210-230 can be repeated to generate one or more new or updated risk compliance scores to be output in operation.);
and indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain entity data of the multiple user entities are necessary to improve predictability of the target variable (BISWAS: col. 18, ll. 25-44: In an embodiment, the regulatory compliance assessment system 520 processes multiple records (e.g., millions of records) to prioritize actions to be taken by the entity to increase or improve an associated risk compliance index score. In an embodiment, one or more machine learning algorithms executed by the machine learning component of the regulatory compliance assessment system 520 to refine the recommendations as data changes over time. The risk compliance profile including the one or more risk compliance index scores and corresponding prioritized actions can enable an entity to take actions based on their identified business risks. Advantageously, the regulatory compliance assessment system 520 iteratively and repeatedly monitors an entity's systems and information in real-time to identify the recommended actions to be executed by an entity (e.g., identify potential non-conformances and associated actions to assist the entity in establishing conformity) and iteratively refine or update the corresponding risk compliance index score for the entity; col. 11, ll. 29-36: In an embodiment, the regulatory compliance assessment system 120 monitors systems and processes of an entity and data from multiple data sources in real-time to refine their actions in view of potential or identified non-conformances. In an embodiment, information associated with the identified actions can be provided by the compliance prediction module 131 to the machine learning component 124);
deploying the trained algorithm to generate the exposure metric representing exposure of the user entity to third party envelopment for the user entity using one or more output categories (BISWAS: col. 5, ll. 4: 6-8: enable the generation of a risk compliance index score associated with the entity in accordance with the methods described; col. 13, ll. 15-20: operations of the method 200 can be performed iteratively, such that operations 210-230 can be repeated to generate one or more new or updated risk compliance scores to be output in operation),
and based thereon determining whether the exposure metric exceeds or subceeds a threshold of fulfillment or nonfulfillment of prescribed actions respective to the user entity (BISWAS: col. 11, ll. 1-12: the compliance prediction module 131 is configured to generate more actions based on the risk compliance index scores to enable an entity prioritize compliance- related activities in view of the identified business risks. For example, a QA function can have a "Process" control type score of 0.95 and a score of 0.31 in an "Investigation" control type. The QA team now has the ability to prioritize the "Process" work ahead of "Investigation" as the risk related to "Process" is more than the "Investigation". In another example, a Facility function can have a score of 0.57 in a "Technology" control type for the same organization; BISWAS: col. 11, ll. 15-28: In the example above, the QA process had a high risk score due to not having a training SOP in place and having training records that were not current. The system can identify a "Create training SOP" action and an "Update training records" action that can be executed to reduce the QA Process risk score.);
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of GONZALEZ, which discloses systems and methods of managing tax liabilities (GONZALEZ ¶[0018]) and avoiding substantial penalties for non-compliance (GONZALEZ ¶[0020]) with the technique of BISWAS, in order to be able to process more records/data to refine recommendations and update risk scores for noncompliance (BISWAS col. 18, ll. 26-44) and to enable refinement of the risk assessments (BISWAS col. 11, ll. 29-36 ).

Regarding claim(s) 12 and 20,
The combination of GONZALEZ, SKALSKI and BISWAS teaches the method or system of claims 11 and 19, respectively.
GONZALEZ does not expressly disclose the following limitations, which BISWAS however, teaches:
wherein the algorithm utilizes records, specific to the user entity, of fulfillment or nonfulfillment of the prescribed actions in prior time intervals to calculate the exposure metric representing exposure of the user entity to third party envelopment in a time interval subsequent to the prior time intervals (BISWAS col. 12, ll. 21-23: collects, from multiple data sources, regulatory-related data associated with an entity; BISWAS: col. 9, ll. 7-12: the data classification module 126 analyzes internal and external audit results including individual procedural Non-Conformance (NC) findings that are accumulated over time; BISWAS col. 5, ll. 30-56: the data monitoring component receives raw regulatory-related data associated with an entity including historical data and can be collected on a periodic and iterative basis to capture changes in the data and enable an updated calculation of the risk compliance index score; BISWAS col. 10, ll. 13-19: base line data sources can include a collection of data obtained over a period of time (e.g., multiple years). Older data can be considered to be less relevant. Accordingly, while calculating the corresponding risk score, the age of the data can be taken into account.).
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of GONZALEZ, which discloses systems and methods of managing tax liabilities (GONZALEZ ¶[0018]) and avoiding substantial penalties for non-compliance (GONZALEZ ¶[0020]) with the technique of BISWAS, in order to be able to process more records/data to refine recommendations and update risk scores for noncompliance (BISWAS col. 18, ll. 26-44) and to enable refinement of the risk assessments (BISWAS col. 11, ll. 29-36 ).

Regarding claim(s) 13,
The combination of GONZALEZ, SKALSKI, and BISWAS teaches the method or system of claims 11 and 12, respectively.
GONZALEZ does not expressly disclose the following limitations, which BISWAS however, teaches:
wherein the algorithm utilizes respective records, associated with multiple respective user entities, of fulfillment or nonfulfillment of the prescribed actions in prior time intervals to calculate the exposure metric representing exposure of the user entity to third party envelopment in a time interval subsequent to the prior time intervals (BISWAS col. 5, ll. 65-67 to col. 6, ll. 1-16: In an embodiment, the data monitoring and extraction module 122 collects raw regulator-related data from one or more data sources that are independent of a specific entity (e.g., company) or specific audit and are generally available in the public domain, examples include FDA warning letters, court-imposed fines and settlements on industry companies.; In an embodiment, the collected and extracted regulatory-related data can be stored in a risk and compliance data store).
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of GONZALEZ, which discloses systems and methods of managing tax liabilities (GONZALEZ ¶[0018]) and avoiding substantial penalties for non-compliance (GONZALEZ ¶[0020]) with the technique of BISWAS, in order to be able to process more records/data to refine recommendations and update risk scores for noncompliance (BISWAS col. 18, ll. 26-44) and to enable refinement of the risk assessments (BISWAS col. 11, ll. 29-36 ).

Regarding claim(s) 15,
The combination of GONZALEZ, SKALSKI, and BISWAS teaches the system of claim 11.
GONZALEZ further discloses:
further comprising, transferring from at least one of the first resource and second resource a contribution to a resource exempted from said prescribed actions (GONZALEZ: ¶[0005]: embodiments automate creating set-asides for tax payments, giving the user a similar experience to an employee of an established business; ¶[0032]: temporary set-aside module 218 (hereafter set-aside module 218); ¶[0042]: integration enables the system to move money on behalf of the worker for set asides; ¶[0057]:the user may have many deductible business expenses that reduce taxable income, and thus reduce the recommended tax set-aside values. Thus, the example tax calculation computer 112 assists the user 102 in identifying deductible business expenses, and in turn reducing the estimated income values and corresponding estimated taxes.).

Regarding claim(s) 16,
The combination of GONZALEZ, SKALSKI, and BISWAS teaches the system of claim 11.
GONZALEZ further discloses:
wherein determining the exposure metric representing exposure of the user entity to third party envelopment comprises forecasting a quantitative impact of the third party envelopment on resources of the user entity (GONZALEZ: ¶[0050]: The example embodiments then calculate or project an annual tax burden of the projected annual income. That is, for example, using the tax tables 234 the example embodiments calculate an annual tax burden based on the projected annual income. Next, the example embodiments project a remaining tax burden based on previous tax payments for the current tax year and a value in the tax set-aside account (e.g., subtract the previous tax payments for the current tax year and a value in the tax set-aside account from the annual tax burden). Next, the example embodiments calculate an expected future income value (e.g., the difference between the annual income value and the new income value). Thereafter, the example embodiments may calculate an adjusted set-aside percentage based on the remaining tax burden and the expected future income, and recommend a tax set-aside value of the new deposit based on the adjusted set-aside percentage and the new deposit.).

Regarding claim(s) 17,
The combination of GONZALEZ, SKALSKI, and BISWAS teaches the system of claim 1.
GONZALEZ further discloses:
wherein fulfillment or nonfulfillment of the prescribed actions is determined for time intervals, and wherein fulfillment of the prescribed actions for any given time interval is required by the third party in a time interval subsequent to the given time interval (GONZALEZ: ¶[00501]: The granularity of the period used to calculate the average periodic income may be any suitable period, such as calendar quarter, month, day, hour, half-hour, minute, and so on; ¶[0041]: In the example systems and methods, the software routines periodically (e.g., daily, multiple times each day) polls the accounts of user 102 looking for new transactions of the primary account; ¶[0072]: period is tax quarters); ¶[0018]: calculating tax liability and setting aside funds to meet periodic tax payments (e.g., quarterly in the United States, biannually in other countries).

Regarding claim(s) 18,
The combination of GONZALEZ, SKALSKI, and BISWAS teaches the system of claim 11.
GONZALEZ further discloses:
wherein a report to the third party of fulfillment or nonfulfillment of the prescribed actions is required of the user entity periodically (GONZALEZ: ¶[0002]: U.S. government fails to collect a portion of taxes due because independent contractors and self-employed individuals fail to properly report and manage their tax responsibilities; ¶[0020]: most revenue collection agencies also require periodic estimated tax payments to be made. In the United States, for example, the Internal Revenue Services (IRS) requires tax payers to make quarterly estimated tax payments. Failure to make periodic estimated tax payments can result in substantial penalties, in some countries approaching 50%; ¶[0021]: describes typical process of collecting data regarding income over the quarter, estimating tax, and making the estimated tax payment for the quarter to the IRS. The process continues once each quarter, with the end-of-tax-year reconciliation at tax time; ¶[0023]: The various example embodiments were developed in the context of periodic estimated tax payments in the United States; the many taxing authorities (e.g., cities, counties, states, and countries) expect periodic estimated tax payments to be made, and the various embodiments are applicable to any such taxing authority, including multiple simultaneous taxing authorities.).

Response to Arguments
35 U.S.C. § 112
Applicant's arguments filed 23 January 2025 have been fully considered but they are not persuasive. Applicants points to paragraphs ¶[0002] and ¶[0125] as support for the term envelopment being definite. This argument has been considered but is unpersuasive. The phrases “envelopments such as” in ¶[0002] and “can be described as envelopment” in ¶[0125] are indications that these are merely illustrative and non-limiting examples of the term. What the term “third-party envelopment” could mean to a person of skill in the art are so numerous that one would not be reasonably apprised of the scope of the invention; and so, the metes and bounds of the claim cannot be determined. According to MPEP 2173.02(I) “if the language of a claim, given its broadest reasonable interpretation, is such that a person of ordinary skill in the relevant art would read it with more than one reasonable interpretation, then a rejection under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph is appropriate.”

35 U.S.C. § 101
Applicant's arguments filed 23 January 2025 have been fully considered but they are not persuasive. Applicant argues at pages 5-7 that the claims do not recite an abstract idea per se and points to USPTO example 39 for support. Unlike the claims in Example 39, claim 1 of the instant application recites “determin[ing] an exposure metric representing exposure of the user entity to third party envelopment” and “directing the user entity to a mitigation service to mitigate the third party envelopment when the exposure metric exceeds the threshold.” MPEP 2106.04(a)(2)(II)(A) states that fundamental economic principles or practices include “mitigating risks”. Example 39 involves a claim that "does not recite any of the judicial exceptions enumerate in the 2019 PEG" and is eligible at step 2A - Prong 1 (Judicial Exception Recited --> No). In contrast, claim 1 recites the abstract idea of mitigating risk.
At pages 7-11, Applicant argues that any abstract idea is integrated into a practical application. At page 9, Applicant argues that the claimed “iterative process necessarily improves accuracy of the algorithm by improving predictability of a target variable, thereby improving the overall function of the computer or computer system.” This argument has been considered but is unpersuasive. Performing an iterative process could be performed by a human with pen and paper and is part of the abstract idea.
Applicant's arguments filed 23 January 2025 have been fully considered but they are not persuasive. At page 9, Applicant argues that any judicial exception is used in conjunction with a particular machine or manufacture that is integral to the claim. This argument has been considered but is unpersuasive. The additional elements in the claims include: a computing system including one or more processor and at least one of a memory device and a non-transitory storage device, wherein said one or more processor executes computer-readable instructions; and a network connection operatively connecting user devices to the computing system and sen[ding] across the network connection to a user device. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008).
At page 10, the Applicant argues that training, via machine learning and using a set of training data, an algorithm to determine an exposure metric representing exposure of the user entity to third party envelopment at a time interval subsequent to the timestamp of the event records and then the system utilizes the trained algorithm to determine whether the exposure metric exceeds or subceeds a threshold of fulfillment or nonfulfillment of prescribed actions respective to the user entity provides a meaningful use of any recited judicial exception that does not monopolize any recited judicial exception. This argument has been considered but is unpersuasive. These recited limitations are part of the abstract idea and are merely use the computer as a tool to carry out the abstract idea and thus do not integrate the abstract idea into a practical application.
In the last paragraph at page 10, the Applicant argues that training the algorithm using an iterative process, predicting being based on at least one output category, testing and comparing the metric predicted during each iteration against a target variable, and indicating via a feedback loop whether modifications to weighs are necessary to improve predictability of the target variable are not insignificant in that they are essential for the algorithm to be able to determine whether the exposure metric excides or subceeds a threshold. This argument has been considered but is unpersuasive. These recited limitations are part of the abstract idea and are merely use the computer as a tool to carry out the abstract idea and thus do not integrate the abstract idea into a practical application.
At page 12, the Applicant argues the claims recite a combination of steps that perform predictions in an unconventional way and so significantly more than a judicial exception. This argument has been considered but is unpersuasive. Determining an exposure metric and the recited steps are part of the abstract idea and merely use the computer as a tool to carry out the abstract idea and thus do not integrate the abstract idea into a practical application.

35 U.S.C. § 103
At page 12-18, Applicant argues Gonzalez, Skalski, and Biswas nor a combination thereof teach or suggest the invention recited in claims 1, 11, and 19. This argument has been considered but is unpersuasive. The combination of GONZALEZ, SKALSKI, and BISWAS teach these limitations, as shown in the rejection under 35 U.S.C. § 103, above.

Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20170337636 A1 teaches a method and apparatus for alerting or notifying a user in real-time to events affecting his current and future wealth projections.
US 20220414790 A1 teaches the use of machine learning to predict tax liability amounts. For example, the machine learning models can determine tax liabilities and loan offers more accurately and efficiently from various data, such as payroll data, direct deposit data, tax withholdings, and PSS transactions (e.g., securities transactions, charitable donations, etc.)
US 20170011377 A1 teaches one or more aggregate reserve accounts may be maintained for a plurality of merchants and/or industry sectors. In yet a further embodiment, one or more reserve accounts may be maintained for each taxing authority or taxing authorities
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.

Any inquiry concerning this communication or earlier communications from the examiner should be directed to BOLKO HAMERSKI whose telephone number is (571)270-7621. The examiner can normally be reached Monday-Friday 10:00 AM to 6:00 PM.
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BOLKO HAMERSKI
Examiner
Art Unit 3694

/BOLKO M HAMERSKI/Examiner, Art Unit 3694
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694