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 Claims
This is in reply to communication filed on 01/16/2024.
Claims 4, 9-10, 13, 74 and 80 have been amended.
Claims 3, 7-8, 16, 19-26, 28-70, 73, 75-79, 81-96 and 98-105 have been cancelled.
Claims 1-2, 4-6, 9-15, 17-18, 27, 71-72, 74, 80 and 97 are currently pending and have been examined.
Information Disclosure Statement (IDS)
The information disclosure statement filed on 08/23/2024 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-6, 9-15, 17-18, 27, 71-72, 74, 80 and 97 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1:
Claims 1-2, 4-6, 9-15, 17-18 and 27 recite a system, which is directed to a machine.
Claims 71-72, 74, 80 and 97 recite a method, which is directed to a process.
Therefore, each claim falls within one of the four statutory categories.
Step 2A, Prong 1 (Is a judicial exception recited?):
The independent claims 1 and 71 recite the abstract idea of disbursing advanced wages to an employee prior to a payment date of scheduled wages for a pay period, see specification [0003]. This idea is described by the steps of
(a) determining an eligibility of the employee for receiving the advanced wages, wherein the advanced wages correspond to cumulative wages accrued by the employee at a given moment during the pay period;
(b) based on the eligibility of the employee, identifying a predictable pattern for i) the amount of scheduled wages received by the employee, ii) the frequency of the scheduled wages received, iii) a rate at which the scheduled wages are accrued during the pay period ("pay rate"), or iv) a combination thereof;
(c) calculating an amount of advanced wages available ("available amount") for disbursement to the employee, the available amount based on i) the cumulative time worked by the employee during the pay period, ii) the predictable pattern, iii) expenditures by the employee, iv) a threshold percentage of the predicted pattern amount of the scheduled wages, v) a threshold amount, or vi) a combination thereof;
(d) verifying an employment status of the employee during the pay period, the employment status corresponding to an active status or an inactive status, wherein an active status corresponds a prediction of the employee receiving the next scheduled wages, and an inactive status corresponds to a prediction of the employee not receiving the next scheduled wages; and
(e) based on the employee having an active status, disbursing to the employee a disbursed amount of the advanced wages up to the available amount;
communication with one or more financial accounts associated with the employee ("employee account"), one or more financial accounts associated with the system ("system account"), an employment email account associated with the employee, a location indicator to detect a location of the employee, receive input from and/or output data to the employee, or any combination thereof, so as to perform operations from one or more of (a) - (e)
(A) These claims recite a certain method of organizing human activity. The claims recite to a certain method of organizing human activity as the above abstract idea limitations are directed to fundamental economic practices or principles of describing disbursing advanced wages to an employee prior to a payment date of scheduled wages for a pay period. The Examiner additionally finds the claims to be similar to an example the courts have identified as being a certain method of organizing human activity:
OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) (a new method of price optimization was found to be a fundamental economic concept); In re Smith, 815 F.3d 816, 818-19, 118 USPQ2d 1245, 1247 (Fed. Cir. 2016),
In re Greenstein, 774 Fed. Appx. 661, 664, 2019 USPQ2d 212400 (Fed Cir. 2019) (non-precedential) (claims to a new method of allocating returns to different investors in an investment fund was a fundamental economic concept)
(B) These claims recite a certain method of organizing human activity. The claims recite to a certain method of organizing human activity as the above abstract idea limitations are directed to managing personal behavior or relationships or interactions between people. The examiner finds the claims to simply recites steps of following rules or instructions to disbursing advanced wages to an employee prior to a payment date of scheduled wages for a pay period. The Examiner additionally finds the claims to be similar to an example the courts have identified as being a certain method of organizing human activity: a series of instructions of how to hedge risk, Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1004 (2010).
Step 2A, Prong 2 (Is the exception integrated into a practical application?):
This judicial exception is not integrated into a practical application because the claims satisfy the following criteria, which indicate that the claims do not integrate the abstract idea into practical application:
The claimed additional limitations are:
Claim 1: system comprising one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to perform operations,
The additional limitations are directed to using a generic computer to process information and perform the abstract idea. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?):
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As for Step 2B analysis, knowing the consideration is overlapping with Step 2A, Prong 2. The Step 2B considerations have already been substantially addressed under Step 2A Prong 2, see Step 2A Prong 2 analysis above. As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
In addition, the dependent claims recite:
Step 2A, Prong 1 (Is a judicial exception recited?):
Dependent claims 2, 4-6, 9-15, 17-18, 27, 72, 74, 80 and 97 recitations further narrowing the abstract idea recited in the independent claims 1 and 71 and therefore directed towards the same abstract idea, as:
Step 2A, Prong 2 and Step 2B:
The dependent claims 2, 4-6, 9-15, 17-18, 27, 72, 74, 80 and 97 further narrow the abstract idea recited in the independent claims 1 and 71 and are therefore directed towards the same abstract idea.
The dependent claims recite the following additional limitations:
Claim 2: system, using a first decision engine by the one or more processors,
Claim 4: system, the first decision engine,
Claim 5: system, the first decision engine, a machine learning model,
Claims 6, 15: system, the machine learning model,
Claims 9, 12, 17, 18, 27: system,
Claim 10: system, the one or more processors,
Claim 11: system, a second decision engine by the one or more processors,
Claim 13: system, the second decision engine
Claim 14: system, the second decision engine, a machine learning model,
Claims 72, 74: a first decision engine,
However, the examiner finds each of these additional elements to be directed to merely “apply it” or applying a generic technology to perform the recited abstract idea of disbursing advanced wages to an employee prior to a payment date of scheduled wages for a pay period, the recitation to the generic computer technology that is being used as a tool to execute the steps that define the abstract idea do not provide for integration at the 2nd prong and do not provide for significantly more at step 2B.
Therefore, the limitations on the invention of claims 1-2, 4-6, 9-15, 17-18, 27, 71-72, 74, 80 and 97, when viewed individually and in ordered combination are directed to in-eligible subject matter.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-2, 4-6, 9-15, 17-18, 27, 71-72, 74, 80 and 97 are rejected under 35 U.S.C 103 as being unpatentable over Dias et al. (US 20220058586 A1) hereinafter “Dias” in view of Krug et al. (US 20220188943 A1) hereinafter “Krug” further in view of Shah et al. (US 20160086261 A1) hereinafter “Shah”.
Regarding claims 1 and 71. Dias discloses a system for disbursing advanced wages to an employee prior to a payment date of scheduled wages for a pay period, the system comprising one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to perform operations including:
(a) determining an eligibility of the employee for receiving the advanced wages, wherein the advanced wages correspond to cumulative wages accrued by the employee at a given moment during the pay period; (Dias, [0066]; “collects work agreement information (i.e. the claimed eligibility) corresponding to the employee from a storage server of an employer of the employee via a network (step 302) … collects work-related information (i.e. the claimed cumulative wages) corresponding to the employee from a client device of the employee on a predetermined time interval basis via the network”)
(c) calculating an amount of advanced wages available ("available amount") for disbursement to the employee, the available amount based on i) the cumulative time worked by the employee during the pay period, ii) the predictable pattern, iii) expenditures by the employee, iv) a threshold percentage of the predicted pattern amount of the scheduled wages, v) a threshold amount, or vi) a combination thereof; (Dias, [0067-0068]; “the computer determines a confidence level regarding work-related activity of the employee corresponding to a specified wage payment type for a current wage pay period based on the analysis of the collected work agreement information and work-related information corresponding to the employee using the machine learning to verify employee eligibility for wage payment (step 308) … determines a proportional wage payment amount for the employee in accordance with the specified wage payment type for the current wage pay period based on the collected work agreement information (step 314)”)
(e) based on the employee having an active status, disbursing to the employee a disbursed amount of the advanced wages up to the available amount; (Dias, [0070]; “the computer transmits a notification to the employee and the employer via the network using the specified communication channel indicating that the order for the proportional wage payment amount was processed by the specified wage payment proxy server (step 322)”)
wherein any one of the one or more processors is configured to be in communication with one or more financial accounts associated with the employee ("employee account") (Dias, [0020]; “[0020] Employer storage server 108 is a network storage device capable of storing any type of data in a structured format or an unstructured format. Employer storage server 108 may represent a plurality of network storage devices owned or operated by one or more employers … Employer storage server 108 may also store employee work-related data, files, applications, programs, and the like”), one or more financial accounts associated with the system ("system account") (Dias, [0017]; “Wage payment proxy server 106 provides real wage payments to the employees”), a location indicator to detect a location of the employee (Dias, [0023]; “The collected work-related information from clients 110, 112, and 114 may include, for example: geolocation data, such as GPS coordinates, identifying where work-related activities are being performed by employees”), a computing interface to receive input from and/or output data to the employee, or any combination thereof, so as to perform operations from one or more of (a) - (e). (Dias, [0050]; “Display 214 provides a mechanism to display information to a user and may include touch screen capabilities to allow the user to make on-screen selections through user interfaces or input data, for example”)
Dias substantially discloses the claimed invention; however, Dias fails to explicitly disclose the “based on the eligibility of the employee, identifying a predictable pattern for i) the amount of scheduled wages received by the employee, ii) the frequency of the scheduled wages received, iii) a rate at which the scheduled wages are accrued during the pay period ("pay rate"), or iv) a combination thereof”. However, Krug teaches:
(b) based on the eligibility of the employee, identifying a predictable pattern for i) the amount of scheduled wages received by the employee, ii) the frequency of the scheduled wages received, iii) a rate at which the scheduled wages are accrued during the pay period ("pay rate"), or iv) a combination thereof; (Krug, [0024]; “an available balance may be determined based on simulation of a payroll process, where the simulation can be executed and/or initiated by a payment service. For example, the simulation may be performed at a payroll system of the enterprise employing a requestor … predicted data that is calculated by the payment service in the absence of real (e.g., actually stored or tracked) data. The predicted data may be calculated based on various suitable algorithms, where those algorithms can be implemented to generate predicted data based on a set of current data for a current pay cycle associated with the request, as well as data associated with previous pay cycles”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include based on the eligibility of the employee, identifying a predictable pattern for i) the amount of scheduled wages received by the employee, ii) the frequency of the scheduled wages received, iii) a rate at which the scheduled wages are accrued during the pay period ("pay rate"), or iv) a combination thereof, as taught by Krug, where this would be performed in order to monetize performed work and activities that correspond to a future paycheck into a payment amount. See Krug [0019].
The combination of Dias in view of Krug substantially discloses the claimed invention; however, the combination fail to explicitly disclose the “verifying an employment status of the employee during the pay period, the employment status corresponding to an active status or an inactive status, wherein an active status corresponds a prediction of the employee receiving the next scheduled wages, and an inactive status corresponds to a prediction of the employee not receiving the next scheduled wages; and an employment email account associated with the employee”. However, Shah teaches:
(d) verifying an employment status of the employee during the pay period, the employment status corresponding to an active status or an inactive status, wherein an active status corresponds a prediction of the employee receiving the next scheduled wages, and an inactive status corresponds to a prediction of the employee not receiving the next scheduled wages; (Shah, [0110]; “amount is determined by underwriting the employer and based on an active employment status (part time or full time) of an employee. Whenever an employee requests a wage advance, an amount based on unearned income is accessed first by the employee before accessing a wage advance amount based on earned but unpaid income”) and an employment email account associated with the employee (Shah, [0082]; “the employee provides data (d) through (h) listed above (that is, date of birth, residence zip code, email address”),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include verifying an employment status of the employee during the pay period, the employment status corresponding to an active status or an inactive status, wherein an active status corresponds a prediction of the employee receiving the next scheduled wages, and an inactive status corresponds to a prediction of the employee not receiving the next scheduled wages; and an employment email account associated with the employee, as taught by Shah, where this would be performed in order to facilitate the accessing of accrued but unpaid earnings. See Shah [0005].
Regarding claims 2 and 72. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 1, wherein
Dias substantially discloses the claimed invention; however, Dias fails to explicitly disclose the “verifying the employment status comprises predicting whether the employee will receive a next scheduled wages using a first decision engine by the one or more processors, wherein an active status corresponds to an employee receiving the next scheduled wages”. However, Krug teaches:
verifying the employment status comprises predicting whether the employee will receive a next scheduled wages using a first decision engine by the one or more processors, wherein an active status corresponds to an employee receiving the next scheduled wages. (Krug, [0024-0025]; “predicted data that is calculated by the payment service in the absence of real (e.g., actually stored or tracked) data. The predicted data may be calculated based on various suitable algorithms, where those algorithms can be implemented to generate predicted data based on a set of current data for a current pay cycle associated with the request, as well as data associated with previous pay cycles … he payment service may calculate an available balance in such a way as to minimize a total possible overpayment resulting from underestimating or overestimating factors impacting payment of employees eligible for advance payment determination by the payment service”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include verifying the employment status comprises predicting whether the employee will receive a next scheduled wages using a first decision engine by the one or more processors, wherein an active status corresponds to an employee receiving the next scheduled wages, as taught by Krug, where this would be performed in order to monetize performed work and activities that correspond to a future paycheck into a payment amount. See Krug [0019].
Regarding claims 4 and 74. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 2
Dias substantially discloses the claimed invention; however, Dias fails to explicitly disclose the “the first decision engine applies one or more employment status data for the employee, the employment status data comprising past scheduled wages data, past employee financial activities data, past disbursed advanced wages data, past restore faults data, or a combination thereof, to predict whether the employee will receive the next scheduled wages”. However, Krug teaches:
the first decision engine applies one or more employment status data for the employee, the employment status data comprising past scheduled wages data, past employee financial activities data, past disbursed advanced wages data, past restore faults data, or a combination thereof, to predict whether the employee will receive the next scheduled wages. (Krug, [0024-0025]; “predicted data that is calculated by the payment service in the absence of real (e.g., actually stored or tracked) data. The predicted data may be calculated based on various suitable algorithms, where those algorithms can be implemented to generate predicted data based on a set of current data for a current pay cycle associated with the request, as well as data associated with previous pay cycles … the payment service may calculate an available balance in such a way as to minimize a total possible overpayment resulting from underestimating or overestimating factors impacting payment of employees eligible for advance payment determination by the payment service”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include the first decision engine applies one or more employment status data for the employee, the employment status data comprising past scheduled wages data, past employee financial activities data, past disbursed advanced wages data, past restore faults data, or a combination thereof, to predict whether the employee will receive the next scheduled wages, as taught by Krug, where this would be performed in order to monetize performed work and activities that correspond to a future paycheck into a payment amount. See Krug [0019].
Regarding claim 5. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 4, wherein
Dias substantially discloses the claimed invention; however, Dias fails to explicitly disclose the “the first decision engine applies the one or more employments status data using a machine learning model”. However, Krug teaches:
the first decision engine applies the one or more employments status data using a machine learning model. (Krug, [0132]; “neural networks can be used to provide prediction services in relation to approvals. Based on machine learning logic approvals can be predicted in a supervised scenario, using past time recordings and approvals as inputs along with non-personal information, such as, work schedules, seasonal business fluctuations, type of work or project performed, etc.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include the first decision engine applies the one or more employments status data using a machine learning model, as taught by Krug, where this would be performed in order to monetize performed work and activities that correspond to a future paycheck into a payment amount. See Krug [0019].
Regarding claim 6. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 5, wherein
Dias substantially discloses the claimed invention; however, Dias fails to explicitly disclose the “the machine learning model incorporates a respective weight for each type of employment status data, wherein each respective weight is determined using trained data of historical employment status data relating to the employee, from a cohort of individuals, or both, wherein each data input is correlated with whether i) the respective individual of the cohort of individuals, or ii) employee, received scheduled wages for a given pay period”. However, Krug teaches:
the machine learning model incorporates a respective weight for each type of employment status data, wherein each respective weight is determined using trained data of historical employment status data relating to the employee, from a cohort of individuals, or both, wherein each data input is correlated with whether i) the respective individual of the cohort of individuals, or ii) employee, received scheduled wages for a given pay period. (Krug, [0024]; “the simulation may be performed based on data that is determined as a basis for performing a real time estimation of an acceptable amount to be allocated to the requestor … The predicted data may be calculated based on various suitable algorithms, where those algorithms can be implemented to generate predicted data based on a set of current data for a current pay cycle associated with the request, as well as data associated with previous pay cycles. Using the current and historical data, predicted data sets can be generated and used in the calculation of the available balance”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include the machine learning model incorporates a respective weight for each type of employment status data, wherein each respective weight is determined using trained data of historical employment status data relating to the employee, from a cohort of individuals, or both, wherein each data input is correlated with whether i) the respective individual of the cohort of individuals, or ii) employee, received scheduled wages for a given pay period, as taught by Krug, where this would be performed in order to monetize performed work and activities that correspond to a future paycheck into a payment amount. See Krug [0019].
Regarding claim 9. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 4,wherein
The combination of Dias in view of Krug substantially discloses the claimed invention; however, the combination fail to explicitly disclose the “the operations further include performing an employment status prediction at the beginning of each pay period”. However, Shah teaches:
the operations further include performing an employment status prediction at the beginning of each pay period. (Shah, [0110]; “amount is determined by underwriting the employer and based on an active employment status (part time or full time) of an employee. Whenever an employee requests a wage advance, an amount based on unearned income is accessed first by the employee before accessing a wage advance amount based on earned but unpaid income”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include the operations further include performing an employment status prediction at the beginning of each pay period, as taught by Shah, where this would be performed in order to facilitate the accessing of accrued but unpaid earnings. See Shah [0005].
Regarding claims 10 and 80. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 4,wherein the one or more processors is further configured to send an alert to the employee, a system administrator, or both, if the employee is predicted not to receive the next scheduled wages. (Dias, [0019]; “The specified communication channel may transmit, for example, wage payment data, acknowledgements, confirmations, notifications, error messages, and the like. Further, the specified communication channel may be encrypted and adhere to specified standards, such as, for example, General Data Protection Regulation or the like, for data security”)
Regarding claim 11. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 1, wherein
Dias substantially discloses the claimed invention; however, Dias fails to explicitly disclose the “verifying the employment status comprises determining an employee risk level using a second decision engine by the one or more processors”. However, Krug teaches:
verifying the employment status comprises determining an employee risk level using a second decision engine by the one or more processors. (Krug, [0060]; “multiple prediction approaches can be run in parallel using a Monte-Carlo method providing a range of possible outcome scenarios that allows customers to make informed trade-offs between a risk of overpayment and maximizing available balances for employees”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include verifying the employment status comprises determining an employee risk level using a second decision engine by the one or more processors, as taught by Krug, where this would be performed in order to monetize performed work and activities that correspond to a future paycheck into a payment amount. See Krug [0019].
Regarding claim 12. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 11, wherein
The combination of Dias in view of Krug substantially discloses the claimed invention; however, the combination fail to explicitly disclose the “a high risk level correlates with a low likelihood of the employee receiving the scheduled wages and an inactive status, and a low risk level correlates with a high likelihood of the employee receiving the scheduled wages and an active status”. However, Shah teaches:
a high risk level correlates with a low likelihood of the employee receiving the scheduled wages and an inactive status, and a low risk level correlates with a high likelihood of the employee receiving the scheduled wages and an active status. (Shah, [0006]; “the present disclosure particularly relates to a system for making payments to employees based on accrued but unpaid earnings before an end of a pay period, where risk factors are taken into account to determine whether payments can be accessed and, if so, the amounts of the payments that can be made. Besides estimating risk based on the current date with respect to a pay period and an hourly rate or salary of an employee, risk can be estimated based upon any of a variety of other factors including, for example, the currency multiple dispensed at a kiosk (e.g., $20 bills, $50 bills) and a number of promised repayments (e.g., 2 repayments, 5 repayments). Also, a fixed fee can be charged for each repayment, and the fee can be adjusted based on risk”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include a high risk level correlates with a low likelihood of the employee receiving the scheduled wages and an inactive status, and a low risk level correlates with a high likelihood of the employee receiving the scheduled wages and an active status, as taught by Shah, where this would be performed in order to facilitate the accessing of accrued but unpaid earnings. See Shah [0005].
Regarding claim 13. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 11
The combination of Dias in view of Krug substantially discloses the claimed invention; however, the combination fail to explicitly disclose the “the second decision engine applies one or more risk factors data comprising transactions at the employee account data, past verifications of the employee email account data, past verifications of the location of the employee data, past restore faults data, income stability data, past employment inactive status data, or a combination thereof, to determine an employee risk level”. However, Shah teaches:
the second decision engine applies one or more risk factors data comprising transactions at the employee account data, past verifications of the employee email account data, past verifications of the location of the employee data, past restore faults data, income stability data, past employment inactive status data, or a combination thereof, to determine an employee risk level. (Shah, [0082]; at the step 550, the third party computer system 110 sends a request that the employee provide the third party computer system with consent to decrypt pre-enrollment data for employee verification and, in response, the employee provides that consent (via the employee computer system 149 or other user access system 150 by which the employee is interfacing the CAN)”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include the second decision engine applies one or more risk factors data comprising transactions at the employee account data, past verifications of the employee email account data, past verifications of the location of the employee data, past restore faults data, income stability data, past employment inactive status data, or a combination thereof, to determine an employee risk level, as taught by Shah, where this would be performed in order to facilitate the accessing of accrued but unpaid earnings. See Shah [0005].
Regarding claim 14. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 13, wherein
The combination of Dias in view of Krug substantially discloses the claimed invention; however, the combination fail to explicitly disclose the “the second decision engine applies the one or more risk factors data using a machine learning model”. However, Shah teaches:
the second decision engine applies the one or more risk factors data using a machine learning model. (Shah, [0263]; “third party using data based predictive analytics can proactively pay bills, top up cellular prepaid, and offer other services from wage employee advances”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include the second decision engine applies the one or more risk factors data using a machine learning model, as taught by Shah, where this would be performed in order to facilitate the accessing of accrued but unpaid earnings. See Shah [0005].
Regarding claim 15. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 14, wherein
Dias substantially discloses the claimed invention; however, Dias fails to explicitly disclose the “the machine learning model incorporates a respective weight for each type of risk factor data, wherein each respective weight is determined using trained data of historical risk factor data relating to the employee, wherein each data input is correlated with whether the employee received the scheduled wages for a given pay period”. However, Krug teaches:
the machine learning model incorporates a respective weight for each type of risk factor data, wherein each respective weight is determined using trained data of historical risk factor data relating to the employee, wherein each data input is correlated with whether the employee received the scheduled wages for a given pay period. (Krug, [0152]; “The implemented prediction model may provide estimation of compensation for performed work that correspond to a real time prediction. The real time prediction of the compensation for performed work and allocated benefits for the period at question may be evaluated according to encoded processing rules at a machine-leaning (ML) payroll prediction model that can be part of the platform service. These processing rules may support an intelligent and automated process execution to serve requests at real time … The estimation of the amount for advance payment is determined as an eligible amount to be allocated to an employee, where the estimation is associated with a relatively low risk of overpayment”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include the machine learning model incorporates a respective weight for each type of risk factor data, wherein each respective weight is determined using trained data of historical risk factor data relating to the employee, wherein each data input is correlated with whether the employee received the scheduled wages for a given pay period, as taught by Krug, where this would be performed in order to monetize performed work and activities that correspond to a future paycheck into a payment amount. See Krug [0019].
Regarding claim 17. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 1, wherein,
Dias substantially discloses the claimed invention; however, Dias fails to explicitly disclose the “identifying the predictable pattern comprises detecting a plurality of deposits to one or more employee accounts, wherein the plurality of deposits are detected to be within a prescribed tolerance of consistency with each other”. However, Krug teaches:
identifying the predictable pattern comprises detecting a plurality of deposits to one or more employee accounts, wherein the plurality of deposits are detected to be within a prescribed tolerance of consistency with each other. (Krug, [0082]; “The financial service is configured to execute financial transactions for the enterprise. In some instances, the determined amount may be transferred according to the logic of the financial service to a bank account, prepaid card, or payroll card of the requestor, or may be provided in other monetary type, such as a check, voucher, or deposit. Other forms of executing a transfer of money from the enterprise to the requestor may be also appreciated as covered by the described transaction execution”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include identifying the predictable pattern comprises detecting a plurality of deposits to one or more employee accounts, wherein the plurality of deposits are detected to be within a prescribed tolerance of consistency with each other, as taught by Krug, where this would be performed in order to monetize performed work and activities that correspond to a future paycheck into a payment amount. See Krug [0019].
Regarding claim 18. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 17, wherein
Dias substantially discloses the claimed invention; however, Dias fails to explicitly disclose the “the prescribed tolerance of consistency is at most from about 1% to about 20%. ”. However, Krug teaches:
the prescribed tolerance of consistency is at most from about 1% to about 20%. (Krug, [0153]; “A predictive model that can be selected and used to determine the acceptable amount at real time applies data evaluation based on measures of the properties in relation to predefined threshold values by the enterprise and the funding provider”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include the prescribed tolerance of consistency is at most from about 1% to about 20%. , as taught by Krug, where this would be performed in order to monetize performed work and activities that correspond to a future paycheck into a payment amount. See Krug [0019].
Regarding claims 27 and 97. The combination of Dias in view of Krug further in view of Shah disclose the system of claim 1, wherein
Dias substantially discloses the claimed invention; however, Dias fails to explicitly disclose the “verifying the employment status comprises verifying the employment email account, verifying the location of the employee via the location indicator, determining an income stability for the employee, determining an employee risk level, verifying a restore record of the employee, receiving a timesheet relating to time worked, performing an employment status prediction, or a combination thereof”. However, Krug teaches:
verifying the employment status comprises verifying the employment email account, verifying the location of the employee via the location indicator, determining an income stability for the employee, determining an employee risk level, verifying a restore record of the employee, receiving a timesheet relating to time worked, performing an employment status prediction, or a combination thereof. (Krug, [0030]; “The platform service may receive requests from end users that provide parameters for requesting compensations for performed work. The platform service may process such requests, verify the requestor's identity, and seamlessly transform the received request based on the implemented logic to acquire needed data from one or more of the systems at the customer environment. That data can then be processed according to implemented prediction model to determine predictable value of performed work and allocated benefits that can be eligibly monetized for the requestor. The platform service provides interfaces to communicate with different systems and applications and exchange data in fast and reliable manner”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dias to include verifying the employment status comprises verifying the employment email account, verifying the location of the employee via the location indicator, determining an income stability for the employee, determining an employee risk level, verifying a restore record of the employee, receiving a timesheet relating to time worked, performing an employment status prediction, or a combination thereof, as taught by Krug, where this would be performed in order to monetize performed work and activities that correspond to a future paycheck into a payment amount. See Krug [0019].
Conclusion
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/AVIA SALMAN/Primary Patent Examiner, Art Unit 3627