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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/26/2026 has been entered.
Response to Amendment
Applicant’s “Amendment” filed on 05/26/2026 has been considered.
Claims 1, 2, 4, 7, 8, 11, 14-16, and 18 are amended. Claims 1-2, 4-9, 11-16, and 18-20 remain pending in this application and an action on the merits follow.
Applicant’s response by virtue of amendment to claims has not overcome the Examiner’s rejection under 35 USC § 101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The amended limitation includes “supplementing…a number of records…that is less than a first threshold”. Specification describes customer records may be accessed to supplement the merchant records and/or if there are too few merchant records (paragraph 29). However, there is no support in the specification what’s the first threshold.
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, 8, and 15 recite the limitation "using the weighted set of transaction factors". There is insufficient antecedent basis for this limitation in the claim.
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, 4-9, 11-16, and 18-20 are rejected under 35 USC 101. The claimed invention is directed to non-statutory subject matter because claims 1, 8, and 15 are directed to an abstract idea without significantly more. Claims 2, 4-7, 9, 11-14, 16, and 18-20 fail to remedy these deficiencies.
The claims 1, 8, and 15 recite analyzing a set of transaction records …to identify a set of transaction factors, supplementing the set of transaction records of other merchants based on at least one of a merchant category and a geographic area; determining the merchant account has a number of records that is less than a first threshold, training a machine learning model using the supplemented set of transaction records, to generate a predictive revenue amount, identifying structures in the set of transaction records, generating a predictive transaction amount using the weighted set of transaction factors, transferring the predictive transaction amount comprises issuing an API call to cause the predicted transaction amount to settle individually in real time, receiving the transaction request, determining a prediction error comprising a difference, re-training the machine learning model using feedback training data that includes the prediction error, based on a determination that a difference between the transaction amount and the predicted transaction amount exceeds a second threshold, displaying a user interface that includes an interactive element indicative of the transaction amount, and ceasing at least one of the transaction amount or additional predicted transaction amount to be transferred to the merchant account comprises preventing predictive revenue deposits until a discrepancy is remedied.
The Claims 1, 8, and 15 recite analyzing, training a machine learning model, generating a predictive transaction amount, transferring the predictive transaction amount via an application programming interface call, receiving the transaction request, re-training the machine learning model, displaying an interactive element indicative of the transaction amount, and ceasing/preventing transferring processing steps as drafted, are processes that under broadest reasonable interpretation, cover performance of managing commercial interactions and fundamental economic practices, but for the recitation of generic computer components. That is, other than reciting “a memory and at least one processor coupled to the memory”, “via a Real-Time Payment (RTP) network”, and “a user device”, nothing in the claim element precludes the steps from practically being performed by organizing human activity for commercial interactions and fundamental economic practices. For example, but for “the memory and the processor”, “via the RTP network”, and “the user device” in the context of these claims encompasses a person manually analyzes the set of collected transactions records to identify a set of transaction factors, trains/utilizes a machine learning model by using the supplemented set of transaction factors, generates a predictive transaction amount using the weighted set of transaction factors, transfers the predictive transaction amount to the merchant account via the application programming interface all to settle individually, re-trains the machine learning model using feedback/prediction error/difference to increase accuracy, receives the transaction request, based on a determination result, displays/shows an notification/indicative element on a board/display/screen, and blocks/rejects/denies/ceases/prevents the transaction amount or additional predicted transaction amounts to being transferred/deposited to the account until a discrepancy is remedied. The ability of a graphical user interface to receive selections and output data is generic. In this case “ceasing/preventing deposits until a corrective action is taken” is a part of commercial interaction. An application program interface is utilized to perform transaction settlement is generic data processing. API call is merely an abstract idea and it does not constitute a technological improvement. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by managing commercial interactions and fundamental economic practices but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The claims 1, 8, and 15 recite identifying and determining steps as drafted, are processes that under broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “the memory and the processor”, “via the RTP network”, and “the user device”, nothing in the claim element precludes the steps from practically being performed in the mind. For example, but for “the memory and the processor”, “via the RTP network”, and “the user device” in the context of these claims encompasses a person manually determines the merchant account has a number of transaction records less than a first threshold, identifies transaction records structures to extract general rules and organize data by similarity, determines a prediction error which is a difference between the transaction amount and the predictive revenue amount exceeds a threshold. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because supplementing step is recited at a high level of generality (i.e., as a general means of collecting supplemented merchant transaction records step) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. This judicial exception is not integrated into a practical application because the claims as a whole merely describe how to generally “apply” the concept of analyzing, supplementing, determining, training, identifying, generating, transferring, receiving, determining, re-training, displaying, and blocking/preventing steps in a computer environment. The processor, the memory, the RTP network, and the user device are recited at a high level of generality and are merely invoked as tools to analyzing, supplementing, determining, training, identifying, generating, transferring, receiving, determining, re-training, displaying, and blocking/preventing steps. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims 1, 8, and 15 are directed to an abstract idea.
The claims 1, 8, and 15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using the processor, the memory, the RTP network, and the user device to perform analyzing, supplementing, determining, training, identifying, generating, transferring, receiving, determining, re-training, displaying, and blocking/preventing steps amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, the claims do not amount to significantly more than the recited abstract idea (Step 2B: NO). The claims 1, 8, and 15 are not patent eligible.
The claims 2, 9, and 16 recite transferring the difference to the merchant account based on an approval/interaction action steps as drafted, are processes that under broadest reasonable interpretation, cover performance of managing commercial interactions and fundamental economic practices. For example, in the context of these claims encompasses a person manually transfers/deposits the difference to the merchant account based on approval interaction. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by managing commercial interactions and fundamental economic practices but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because descriptive content in claims 2, 9, and 16 further limit the abstract idea but not make it less abstract. Thus, the claim 2, 9, and 16 are directed to an abstract idea.
There are no additional claim element limitations recited in the claims 2, 9, and 16. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claims 2, 9, and 16 are not patent eligible.
The claims 4, 11, and 18 recite updating the set of the transaction records, generating an updated set of weighted transaction factors, and re-training the machine learning model steps as drafted, are processes that under broadest reasonable interpretation, cover performance of managing commercial interactions and fundamental economic practices. For example, in the context of these claims encompasses a person manually updates transaction data and generates an updated set of weighted transaction factors to train/re-train the machine learning model/algorithms. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by managing commercial interactions and fundamental economic practices but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because descriptive content in claims 4, 11, and 18 further limit the abstract idea but not make it less abstract. Thus, the claim 4, 11, and 18 are directed to an abstract idea.
There are no additional claim element limitations recited in the claims 4, 11, and 18. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claims 4, 11, and 18 are not patent eligible.
The claims 5, 12, and 19 recite generating a second predictive revenue amount to a second calendar date, and transferring the second predictive revenue amount steps as drafted, are processes that under broadest reasonable interpretation, cover performance of managing commercial interactions and fundamental economic practices. For example, in the context of these claims encompasses a person manually generates a second predictive revenue amount to a second calendar date and deposits the second predictive revenue amount. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by managing commercial interactions and fundamental economic practices but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because descriptive content in claims 5, 12, and 19 further limit the abstract idea but not make it less abstract. Thus, the claim 5, 12, and 19 are directed to an abstract idea.
There are no additional claim element limitations recited in the claims 5, 12, and 19. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claims 5, 12, and 19 are not patent eligible.
The claims 6 and 13 recite generating a second predictive revenue amount to a second range of calendar dates, and transferring the second predictive revenue amount steps as drafted, are processes that under broadest reasonable interpretation, cover performance of managing commercial interactions and fundamental economic practices. For example, in the context of these claims encompasses a person manually generates a second predictive revenue amount to a second range of calendar dates and deposits the second predictive revenue amount. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by managing commercial interactions and fundamental economic practices but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because descriptive content in claims 6 and 13 further limit the abstract idea but not make it less abstract. Thus, the claim 6 and 13 are directed to an abstract idea.
There are no additional claim element limitations recited in the claims 6 and 13. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claims 6 and 13 are not patent eligible.
Claims 7, 14, and 20, disclose insignificant helpful content to further describe content, such as pre-training the machine learning model using a second set of weighted transactions factors corresponding to a second merchant account which is merely descriptive content to further limit the abstract idea but not make it less abstract. Thus, the claims 7, 14, and 20 are directed to an abstract idea.
This judicial exception is not integrated into a practical application because descriptive content in claims 7, 14, and 20 further limit the abstract idea but not make it less abstract. Thus, the claim 7, 14, and 20 are directed to an abstract idea.
There are no additional claim element limitations recited in the claims 7, 14, and 20. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B: NO). The claims 7, 14, and 20 are not patent eligible.
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 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.
Claims 1, 4-8, 11-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2020/0357053 to Masson et al., in view of Canadian Patent No. CA 2,845,743 to Erenrich, and further in view of Japan Patent No. JP 4,018,418 to Kanemori et al.
With regard to claims 1, 8, and 15, Masson discloses a system, comprising:
a memory (Fig. 2, backend server); and
at least one processor coupled to the memory and configured to (Fig. 2, origination processor):
prior to receiving a transaction request from a merchant account (The origination processor 600 may furthermore update all of the datasets 601-602, 611-12, 614, 624 to update PDs, potential interest rates, and forecasted revenues based upon more recent POS data. Examiner notes that current POS data/transaction amount is considered as “a transaction request from a merchant account”, paragraph 123 and abstract), analyzing a set of transaction records corresponding to the merchant account to identify a set of transaction factors associated with the merchant account, wherein the set of transaction records correlates a plurality of time periods with respective transaction amounts, wherein the set of transaction factors is associated with revenue trends of the merchant account, (paragraphs 5, 53, 113, and 119, In addition to retrieving the historical POS data, a stream of comprising the time of year (e.g., week of year, season, etc.) and zip codes may be included as inputs to a dense layer of the neural network 622. Other additional data streams are contemplated to train the neural network 622 such as tips, taxes, number of guests, and other available POS data. Restaurant category (e.g. type) and metropolitan statistical area may also be employed, thereby enabling the recurrent neural network 622 to learn about typical seasonal patterns that different restaurants in different geographies experience. Examiner notes that the machine learning model analyzes historical POS data associated with a type of restaurant during the multiple duration of periods based on factors, such as seasonal and geographic (e.g., metropolitan, rural, tourist area, etc.), which is considered as “analyzing a set of transaction records corresponding to the merchant account to identify a set of transaction factors associated with the merchant account, wherein the set of transaction records correlates a plurality of time periods with respective transaction amounts and wherein the set of transaction factors is associated with revenue trends of the merchant account”), and wherein the transaction request comprises a transaction amount (The revenue forecaster employs the historical POS data to predict future POS data for establishments corresponding to the each of the subscribers and employs the future POS data to generate predicted total revenues corresponding to the each of the subscribers over a payback period. Examiner notes that future POS data/amount is considered as “the transaction request comprises a transaction amount”, paragraph 123 and abstract);
supplementing the set of transaction records with transaction records of other merchants designated as being similar to the merchant account based on at least one of (i) a merchant category code and (ii) a geographic area (paragraphs 119 and 120, Other additional data streams are contemplated to train the neural network 622 such as tips, taxes, number of guests, and other available POS data. Restaurant category (e.g. type) and metropolitan statistical area may also be employed, thereby enabling the recurrent neural network 622 to learn about typical seasonal patterns that different restaurants in different geographies experience. );
training a machine learning model using the supplemented set of transaction records to generate a predicted revenue amount for a time period specific to the merchant account (paragraphs 9, 113, 115, 117, 119, and 133, What is also needed is a system for underwriting capital offers to SMBs that compares a given business with other similar businesses operating in the same geographic area. As one skilled in the art will appreciate, such a technique, relaxed LASSO selects a subset of relevant variables for use that advantageously simplifies the logistic regression analysis and results in shorter training time because redundant and/or irrelevant variables are eliminated without sacrificing significant accuracy. The rate processor 613 then generates a PD dataset 611 for the open restaurants that comprises daily values of the reduced set of metrics yielded from employing relaxed LASSO. In other words, the recurrent neural network 622 is trained to estimate a future stream of POS revenue for a restaurant as a function of its immediately preceding stream, by training the recurrent neural network 622 using all historical POS streams and optional additional data such as location identifiers (e.g., zip codes), restaurant category, time of year indications, and restaurant category corresponding to both currently open and closed restaurants. Examiner notes that all historical POS streams and optional additional data during a period corresponding to restaurants are used to train a machine learning model to generate a predicted revenue, which is considered as “training a machine learning model using the supplemented set of transaction records to generate a predicted revenue amount for a time period specific to the merchant account”);
generate a predicted transaction amount, using the weighted set of transaction factors, for a specified time period by applying the revenue trends and the specified time period to the machine learning model (paragraphs 113, 115, 117, 119, 128, and 133, In one embodiment the specified period is 270 days. The recurrent neural network 622 is trained to estimate a future stream of POS revenue. In addition to retrieving the historical POS data, a stream of comprising the time of year (e.g., week of year, season, etc.) and zip codes may be included as inputs to a dense layer of the neural network 622.);
based on the generation of the predicted amount, transfer the predicted transaction amount to the merchant account via a Real-Time Payment (RTP) network such that the predicted transaction amount is available to the merchant account prior to receiving the transaction request (abstract, paragraphs 12, 97 ad 121-122, The offer processor is configured to generate and transmit the capital product offers corresponding to the each of the subscribers, where the capital product offers comprise the payback period, the prices, and maximum dollar amounts that are a percentage of the predicted total revenues. The capital origination processor 420 may periodically analyze the historical POS data along with subscriber data, as will be described in more detail below, to determine establishments that meet criteria to proffer capital product offers. the capital product offers comprise the payback period, the prices, and maximum dollar amounts that are a percentage of the predicted total revenues. If a given restaurant elects to participate, the offer processor 630 then instructs the payment processor (via TBUS) to originate (i.e., disburse) the specified amount to the restaurant. Examiner notes that the disbursed specified amount is transferred to the restaurant based on the predicted revenues and the disbursed specific amount is funded to the restaurant prior to the future POS data, which is considered as “based on the generation of the predicted amount, transfer the predicted transaction amount to the merchant account via a Real- Time Payment (RTP) network such that the predicted transaction amount is available to the merchant account prior to receiving the transaction request”), wherein transferring the predicted transaction amount comprises issuing an application programming interface (API) call to a banking system via the RTP network to cause the predicted transaction amount to settle individually in real time with immediate finality (paragraphs 57 and 60, The third party-based terminals 241 may comprise desktop computers, laptop, computers, smartphones, or tablets that are running stand-alone third-party applications or web-enabled third-party applications that provide for communication with the backend server 270 for purposes of order entry, status updates, and optionally, payment processing via a proprietary application programming interface (API) 242.);
in response to transferring the predicted transaction amount, receive the transaction request corresponding to the merchant account via the RTP network (abstract, paragraphs 122-123, the offer processor 630 then instructs the payment processor (via TBUS) to originate (i.e., disburse) the specified amount to the restaurant, and to begin periodic holdbacks of the restaurant's processed credit card sales. Examiner notes that recent POS data and future restaurant's processed credit card sales can be considered as “receive the transaction request corresponding to the merchant account via the RTP network”);
determine whether the transaction amount matches the predicted revenue amount (paragraph 124, The offer processor 630 is also configured to compare actual revenue to forecasted revenue);
subsequent to the reception of the transaction request, causing, via the RTP network and based on a determination that a difference between the transaction amount and the predicted transaction amount exceeds a second threshold, a user device to display a user interface that includes an interactive element indicative of the transaction amount (paragraphs 122, 124-127, and 138, Alternatively, the offer processor 630 may contact those selected restaurants via email, regular mail, telephone, or in-person. Accordingly, the restaurant owners are tendered an offer for capital having a maximum loan amount, a fixed payback term, and an interest rate as described above. The touchscreen display 1214 may comprise an owner identification area 1214.1, an owner photo area 1214.3, a terminal configuration area 1214.2, and a capital product offer area 1214.4.); and
cease, via the RTP network and based on the interaction with the interactive element, at least one of the transaction amount or additional predicted transaction amounts from being transferred to the merchant account responsive to the difference of the subsequently received transaction request (paragraphs 124-127, The offer processor 630 is also configured to compare actual revenue to forecasted revenue and updated PDs with previous PDs and to automatically generate engagement instructions for participating restaurants whose PD has increased by more than a threshold amount and/or whose total predicted revenue falls by a specified percentage below a previously predicted revenue. Advantageously, subscription service field representatives are automatically alerted to service restaurant subscribers that at more at risk for repayment. The offer processor 630 may be configured to withdraw offers to one or more selected restaurants that have yet to elect to participate in capital product offers, where the withdrawals are based upon increased PDs and/or decreased POS revenue. The offer processor 630 may be configured to increase or decrease the maximum offer amount based upon updated predicted total revenues. Examiner notes that the offer processor compares actual revenue to forecasted revenue and updated PDs with previous PDs and the subscription service field representatives are automatically alerted based on PD has increased by more than a threshold amount and/or whose total predicted revenue falls by a specified percentage below a previously predicted revenue based on the total predicted revenue falls by a specified percentage below a previously predicted revenue, and the offer processor/server decides to take action to withdraw offers (i.e., additional predicted transaction amounts) that can disburse the offer amount to the merchant/restaurant based upon increased PDs and/or decreased POS revenue, which is considered as “subsequent to the reception of the transaction request…display an alert of the transaction amount to take an interaction, and cease, via the RTP network and based on the interaction with the alert, at least one of the transaction amount or additional predicted transaction amounts from being transferred to the merchant account responsive to the difference of the subsequently received transaction request”) wherein the ceasing comprises preventing predictive revenue deposits via the RTP network until a discrepancy corresponding to the difference is remedied (paragraph 122, the selected restaurants are allowed 1-3 days to accept the offer and specify an amount of capital to be disburse that does not exceed the maximum offer amount. If a given restaurant elects to participate, the offer processor 630 then instructs the payment processor (via TBUS) to originate (i.e., disburse) the specified amount to the restaurant).
However, Masson disclose the interaction is performed by the offer server/processor, however, Masson does not disclose supplementing the set of transaction records with transaction records of other merchants designated as being similar to the merchant account based on at least one of (i) a merchant category code and (ii) a geographic area based on determining that the set of transaction records includes a number of records corresponding to the merchant account that is less than a first threshold; wherein the training comprises identifying structures present in the set of transaction records to extract general rules that reduce redundancy and organize data by similarity; determining a prediction error comprising a difference between the transaction amount and the predicted transaction amount; and re-training the machine learning model using feedback training data that includes the prediction error to increase accuracy of subsequent predicted revenue amounts generated for subsequent time periods.
However, Erenrich teaches supplementing the set of transaction records with transaction records of other merchants designated as being similar to the merchant account based on at least one of (i) a merchant category code and (ii) a geographic area based on determining that the set of transaction records includes a number of records corresponding to the merchant account that is less than a first threshold (After a user selects a merchant ID set 210 as an exemplar merchant ID set 210(0), other merchant ID sets 210 may be considered as candidate merchant ID sets 210(1) through 210(M-1). The user selects the exemplar merchant ID set 210(0) as being representative of the characteristics of the company to be resolved. The exemplar merchant ID set may include a large number of transaction records 215. A large number of transaction records 215 may provide aggregates, such as the average transaction size, that are more accurate than merchant ID sets 210 with fewer transaction records 215. Other factors, such as geographic locations, the merchant ID string, or other business heuristics may also guide the selection of the exemplar merchant ID set 210(0) from the available merchant ID sets 210. Paragraph 35); wherein the training comprises identifying structures present in the set of transaction records to extract general rules that reduce redundancy and organize data by similarity (Existing techniques rely upon simple tests, such as string comparisons between an attribute in a database of transaction records to detect similarities between groups of transaction records. Transaction records including attribute strings that meet a measure of similarity are then aggregated together for analysis, paragraph 4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the merchant cash advance offers system of Masson to include, supplementing the set of transaction records with transaction records of other merchants designated as being similar to the merchant account based on at least one of (i) a merchant category code and (ii) a geographic area based on determining that the set of transaction records includes a number of records corresponding to the merchant account that is less than a first threshold; wherein the training comprises identifying structures present in the set of transaction records to extract general rules that reduce redundancy and organize data by similarity, as taught in Erenrich, in order to improve the process of identifying related transaction records (Erenrich, abstract).
However, Kanemori teaches determining a prediction error comprising a difference between the transaction amount and the predicted transaction amount (The actual total sales amount by a plurality of service devices is input, the input total sales amount and the calculated total sales prediction amount are compared with each other, and an error between the total sales amount and the total sales prediction amount is predetermined. Paragraph 80); and
re-training the machine learning model using feedback training data that includes the prediction error to increase accuracy of subsequent predicted revenue amounts generated for subsequent time periods (If the error is greater than or equal to the error, the prediction coefficient is corrected by performing a predetermined calculation using the total sales amount and the exchange amount. Therefore, by using the corrected prediction coefficient, there is an effect that a sales prediction device, a sales prediction method, or a program capable of predicting the total sales amount by a plurality of service devices with higher accuracy can be obtained. paragraph 80);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the merchant cash advance offers system of Masson to include, determining a prediction error comprising a difference between the transaction amount and the predicted transaction amount; and re-training the machine learning model using feedback training data that includes the prediction error to increase accuracy of subsequent predicted revenue amounts generated for subsequent time periods, as taught in Kanemori, in order to easily predict a total sales amount (Kanemori, background of the invention).
With regard to claims 4, 11, and 18, Masson discloses update the set of transaction records to include the transaction amount (paragraph 97, new POS and subscriber data); and
generate an updated set of weighted transaction factors using the transaction amount (paragraph 123, The origination processor 600 may furthermore update all of the datasets 601-602, 611-12, 614, 624 to update PDs, potential interest rates, and forecasted revenues based upon more recent POS data.), wherein the machine learning model is re-trained using the updated set of weighted transaction factors (paragraph 97, The capital origination processor 420 may moreover regularly update establishments that meet the criteria and their terms for cash advances based upon new POS and subscriber data, and may tender or retract capital product offers and generate engagement instructions for POS subscription service representatives based upon these updates).
With regard to claims 5, 12, and 19, Masson discloses the predicted revenue amount corresponds to a first calendar date and wherein the at least one processor is further configured to: generate a second predicted revenue amount using the machine learning model, wherein the second predicted revenue amount corresponds to a second calendar date; and transfer the second predicted revenue amount to the merchant account via the RTP network on the second calendar date (abstract, paragraph 121-122 and 136, predicted daily revenue).
With regard to claims 6 and 13, Masson discloses the predicted revenue amount corresponds to a first range of calendar dates and wherein the at least one processor is further configured to: generate a second predicted revenue amount using the machine learning model, wherein the second predicted revenue amount corresponds to a second range of calendar dates; and transfer the second predicted revenue amount to the merchant account via the RTP network within the second range of calendar dates (abstract, paragraphs 119 and 121-122, Another embodiment contemplates a specified period of 360 days).
With regard to claims 7, 14, and 20, Masson discloses to training the machine learning model, the at least one processor is further configured to: training the machine learning algorithm using a second set of weighted transaction factors corresponding to a second merchant account (paragraphs 97 and 113, capital products for each of the establishments. The metric variable values for each date, along with restaurant identifier, category, season for the data (e.g., week of the year), and ownership structure are stored in the PD dataset 611 as predictor variables and open/closed status of the restaurants are stored as outcome variables corresponding to the predictor variables.).
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2020/0357053 to Masson et al., Canadian Patent No. CA 2,845,743 to Erenrich, and Japan Patent No. JP 4,018,418 to Kanemori et al., and further in view of U.S. Patent Application Publication No. 2014/0358766 to Nayyar et al.
With regard to claims 2, 9, and 16, the combination of references discloses transferring action is based on an interaction with a different interactive element of the user interface (Rephlo, paragraph 51, The account holder may then confirm or adjust the determined recurring financial transactions and/or income at block 212. manual input income data to an end user where an account holder may approve, deny or modify the transmitted data). However, the combination of references does not disclose transferring the difference between the transaction amount and the predictive transaction amount to the merchant account via the RTP network.
However, Nayyar teaches transferring the difference between the transaction amount and the predictive transaction amount to the merchant account via the RTP network (For example, if the repayment amount is $150, but the merchant has only $50 left in the account, payment service provider may deduct $50 first and then deduct $100 as a catch-up repayment whenever $100 becomes available in the merchant's account, paragraphs 43 and 64-67).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of references to include, transferring the difference between the transaction amount and the predictive transaction amount to the merchant account via the RTP network, as taught in Nayyar, in order to implement working capital for merchants (Nayyar, paragraph 3).
Response to Arguments
Applicants' arguments filed on 05/26/2026 have been fully considered but they are not fully persuasive especially in light of the new prior art applied in the rejections.
Applicants remark that “the combination of references does not disclose supplementing the set of transaction records with transaction records of other merchants designated as being similar to the merchant account based on at least one of (i) a merchant category code and (ii) a geographic area based on determining that the set of transaction records includes a number of records corresponding to the merchant account that is less than a first threshold; wherein the training comprises identifying structures present in the set of transaction records to extract general rules that reduce redundancy and organize data by similarity; wherein transferring the predicted transaction amount comprises issuing an application programming interface (API) call to a banking system via the RTP network to cause the predicted transaction amount to settle individually in real time with immediate finality; determining a prediction error comprising a difference between the transaction amount and the predicted transaction amount; re-training the machine learning model using feedback training data that includes the prediction error to increase accuracy of subsequent predicted revenue amounts generated for subsequent time periods; wherein the ceasing comprises preventing predictive revenue deposits via the RTP network until a discrepancy corresponding to the difference is remedied”.
Examiner directs Applicants' attention to the office action above.
Applicants remark that “the proposed amendment of the claims has overcome the Examiner’s rejection under 35 USC § 101”.
Examiner directs Applicants' attention to the office action above.
Applicants remark that “claims recites a specific, real-time remedial control. This is analogous to USPTO example 47, where eligibility turns on detecting an anomaly and taking real-time remedial action”.
Examiner does not agree. USPTO example 47 discloses detecting malicious network packets and blocking future traffic. The claimed invention discloses preventing deposits until corrective actions are taken. The scope of claims and the example 47 are not similar. Therefore, the claims 1, 8, and 15 are not patent eligible.
Conclusion
Please refer to form 892 for cited references.
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/ARIEL J YU/Primary Examiner, Art Unit 3627
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