DETAILED ACTION
This Non-Final Office Action is in response to the application filed on 05/20/2022, the Amendment & Remark filed on 12/16/2025 and the Request for Continued Examination filed on 12/16/2025.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/16/2025 has been entered.
Specification
The amendment filed 12/16/2025 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows:
Amendment to Paragraph 0044 added new definition (“or deficits”) to the term “shortfalls”, which introduces new matter into the disclosure.
Amendment to Paragraphs 0056 and 0057 narrowed the term “component” to “platform”, which introduces new matter into the disclosure.
Amendment to Paragraph 0071 added new scope (“or mitigate”) to the limitation “to address a future cash flow shortfall…”, which introduces new matter into the disclosure.
Amendment to Paragraph 0085 added new scope (“predetermined”) to the limitation “a certain error threshold”, which introduces new matter into the disclosure.
Amendment to Paragraph 0089 added new scope (“providing an extended term”) to the limitation “the cash flow forecast engine 110 will factor this into the prediction and not expect to receive that payment on the date”, which introduces new matter into the disclosure.
Amendment to Paragraph 0099 added new scope (“subscribing by”) to the limitation “the financial products suitable for the primary entity…”, which introduces new matter into the disclosure.
Applicant is required to cancel the new matter in the reply to this Office Action.
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, 4-8, 12-18, 21 and 41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As an initial matter, the claims as a whole are to a process, an apparatus and a manufacture, which falls within one or more statutory categories. (Step 1: YES) The recitation of the claimed invention is then further analyzed as follow, in which the abstract elements are boldfaced.
The claims recite:
A computer implemented method comprising:
determining, by a cash flow forecast engine of the capital management platform, a set of training cash flow forecasts, each training cash flow forecast being based on financial data associated with an entity of a plurality of entities for a first time frame;
wherein determining, by the cash flow forecast engine, each training cash flow forecast for the first time frame for each entity of the plurality of entities comprises:
assessing the financial data associated with the entity to determine predicted cash flow within the first time frame;
determining a first trend component based on the financial data associated with the entity for the first time frame, wherein the first trend component is indicative of a trend in the financial data associated with the entity and wherein the first trend component comprises a first trend model;
removing the trend component from the financial data associated with the entity for the first time frame to provide de-trended financial data associated with the entity;
determining a first seasonality component based on the de-trended financial data associated with the entity, wherein the first seasonality component is indicative of seasonal variation in the financial data associated with the entity, wherein the first seasonality component comprises one or more first seasonality coefficients; and
using the first trend model and the one or more first seasonality coefficients to determine the training cash flow forecast;
determining a training dataset comprising the set of training cash flow forecasts for the plurality of entities, wherein the training dataset comprises, for each training cash flow forecast, corresponding first sub-time frames, first cash flow surplus or deficit predictions, and one or more first recommendations for each first sub-time frame,
wherein the one or more first recommendations comprise recommendations to adjust the financial data associated with the entity for each of the training cash flow forecasts to manage the training cash flow forecast during the one or more first sub-time frames
training one or more recommendation models using training dataset, to generate respective one or more trained recommendation models;
determining, by the cash flow forecast engine, a baseline cash flow forecast for a second time frame based on financial data associated with a primary entity for the second time frame;
determining, from the baseline cash flow forecast by the cash flow forecast engine, one or more second sub-time frames of the second time frame where cash flow is predicted to be in surplus or deficit; and
based on the determined baseline cash flow forecast and the determined second sub-time frames, generating by a recommendation engine of the capital management platform, one or more second recommendations to improve capital management for the primary entity, wherein the recommendation engine comprises one or more trained recommendation model;
wherein determining, by the cash flow forecast engine, the baseline cash flow forecast for the second time frame comprises:
assessing the financial data associated with the primary entity to determine predicted cash flow within the second time frame.
determining a second trend component based on the financial data associated with the primary entity for that second time frame, wherein the second trend component is indicative of a trend in the financial data associated with the primary entity and wherein the second trend component comprises a second trend model;
removing the second trend component from the financial data associated with the primary entity for the second time frame to provide de-trended financial data associated with the primary entity;
determining a second seasonality component based on the de-trended financial data associated with the primary entity, wherein the second seasonality component is indicative of seasonal variation in the financial data associated with the primary entity, wherein the second seasonality component comprises one or more seasonality coefficients;
using the second trend model and the one or more second seasonality coefficient to determine the baseline cash flow forecast;
wherein the one or more second recommendations generated by the recommendation engine comprise recommendations to adjust the financial data associated with the primary entity to manage the predicted cash flow during the one or more second sub- time frames;
generating, by the capital management platform, a cash flow forecast tool within a graphical user interface (GUI) of a display device, wherein the cash flow forecast tool depicts a representation of the baseline cash flow forecast and the one or more second recommendations;
in response to detecting user selection of one or more of the one or more recommendations using the cash flow forecast tool, adjusting the financial data associated with the primary entity in accordance with the selected one or more suggestions;
determining a modified cash flow forecast for the second time frame based on the adjusted financial data; and
depicting a representation of the modified cash flow forecast within the cash flow forecast tool.
wherein the financial data associated with the primary entity comprises transactions between the primary entity and one or more other entities.
wherein determining, by the cash flow forecast engine, the baseline cash flow forecast comprises: determining the second seasonality component based on a first periodicity in the financial data associated with the primary entity; determining a fitness metric by comparing the financial data associated with the primary entity with the second trend model and the second seasonality component; responsive to the determined fitness metric being below a predetermined fitness threshold, determining a third seasonality component based on a second periodicity in the financial data associated with the primary entity; and projecting the second trend component and the third seasonality component to the second time frame to determine the baseline cash flow forecast.
wherein the first periodicity is larger than the second periodicity.
predicting, by the cash flow forecast engine, a probability of receiving payment of each invoice having a due date in the second time frame and determining a modified cash flow forecast for the second time frame based on the predictions.
wherein predicting a probability of receiving payment of each invoice having a due date in the second time frame comprises determining an action score for a secondary entity associated with each invoice, wherein the action score is based on historic payment related data of the respective secondary entity, and determining the probability of the second entity paying the invoice within a given payment period, wherein the given period falls within the second time frame.
wherein the given period is a specific day within the second time frame.
wherein the one or more second recommendations include one or more of seeking early payment of an outstanding invoice, seeking an extended term for payment of an outstanding invoice, subscribing to a financial product, varying terms of at least one of the one or more transactions.
wherein the one or more second recommendations are generated by the recommendation engine further based on one or more capital management target parameters.
wherein the capital management target parameters comprise one or more of: debt to equity ratio for the entity, weighted average cost of capital to the entity, debt coverage ratio for the entity, number of debtor days for the entity or number of creditor days for the entity.
automatically executing, by the recommendation engine, at least one of the one or more second recommendations based on the target parameters.
analysing, by a retrospective training engine of the capital management platform, the automatically executed one or more second recommendations to determine whether the executed second recommendations mitigated the forecasted one or more cash flow shortfall periods or one or more cash flow excess periods; and revising the recommendation engine based on the analysis.
wherein revising the recommendation engine comprises further training the one or more trained recommendation model based on the analysis.
one or more processors; a memory in communication with the one or more processor, the memory comprising program code which when executed by the one or more processors configures the one or more processors to: determine, by a cash flow forecast engine of the capital management platform, a baseline cash flow forecast …(similar to that of claim 1)
A non transitory machine-readable medium storing computer readable code, which when executed by one or more processors is configured to perform operations including: determining, by a cash flow forecast engine of the capital management platform, a baseline cash flow forecast …(similar to that of claim 1)
Based on the limitations above, the claims describe a process that covers providing recommending to improve capital management. Providing recommending to improve capital management is considered to be a commercial interaction, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. As such, the claim(s) recite(s) a Judicial Exception. (Step 2A prong one: Yes)
This analysis then evaluates whether the claims as a whole integrates the recited Judicial Exception into a practical application of the exception. In particular, the claims recite the additional element(s) of “computer implemented method”, “… platform” “by … engine”, “tool within a graphical user interface (GUI) of a display device”, “one or more processors; a memory in communication with the one or more processor, the memory comprising program code which when executed by the one or more processors configures the one or more processors to: …” and “A machine-readable medium storing computer readable code, which when executed by one or more processors is configured to perform operations” as a mere tool to perform the steps of the Judicial Exception, which encompasses no more than Mere Instruction to Apply.
For example, the limitation “determining, by a cash flow forecast engine of the capital management platform, a set of training cash flow forecasts, each training cash flow forecast being based on financial data associated with an entity of a plurality of entities for a first time frame” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining the set of training cash flow forecast;
the limitation “wherein determining, by the cash flow forecast engine, each training cash flow forecast for the first time frame for each entity of the plurality of entities comprises: assessing the financial data associated with the entity to determine predicted cash flow within the first time frame” encompasses no more than generically invoking a processor to apply the Judicial Exception step of assessing the financial data to determine the predicted capital flow within the first time frame;
the limitation “determining a first trend component based on the financial data associated with the entity for the first time frame, wherein the first trend component is indicative of a trend in the financial data associated with the entity and wherein the first trend component comprises a first trend model” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining the first trend component based on the financial data;
the limitation “removing the trend component from the financial data associated with the entity for the first time frame to provide de-trended financial data associated with the entity” encompasses no more than generically invoking a processor to apply the Judicial Exception step of removing the trend component from the financial data to provide de-trended financial data;
the limitation “determining a first seasonality component based on the de-trended financial data associated with the entity, wherein the first seasonality component is indicative of seasonal variation in the financial data associated with the entity, wherein the first seasonality component comprises one or more first seasonality coefficients” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining the first seasonality component based on the de-trended financial data;
the limitation “using the first trend model and the one or more first seasonality coefficients to determine the training cash flow forecast” encompasses no more than generically invoking a processor to apply the Judicial Exception step of using the first trend model and the one or more first seasonality coefficients to determine the training cash flow forecast;
the limitation “determining a training dataset comprising the set of training cash flow forecasts for the plurality of entities, wherein the training dataset comprises, for each training cash flow forecast, corresponding first sub-time frames, first cash flow surplus or deficit predictions, and one or more first recommendations for each first sub-time frame” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining a training dataset;
the limitation “wherein the one or more first recommendations comprise recommendations to adjust the financial data associated with the entity for each of the training cash flow forecasts to manage the training cash flow forecast during the one or more first sub-time frames” encompasses no more than generically invoking a processor to apply the Judicial Exception step of providing recommendation to adjust the financial data associated with the entity;
the limitation “training one or more recommendation models using training dataset, to generate respective one or more trained recommendation models” encompasses no more than generically invoking a processor to apply the Judicial Exception step of training one or more recommendation model using training data;
the limitation “determining, by the cash flow forecast engine, a baseline cash flow forecast for a second time frame based on financial data associated with a primary entity for the second time frame” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining the baseline cash flow;
the limitation “determining, from the baseline cash flow forecast by the cash flow forecast engine, one or more second sub-time frames of the second time frame where cash flow is predicted to be in surplus or deficit” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining the one or more sub-time frames;
the limitation “based on the determined baseline cash flow forecast and the determined second sub-time frames, generating by a recommendation engine of the capital management platform, one or more second recommendations to improve capital management for the primary entity, wherein the recommendation engine comprises one or more trained recommendation model” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating the one or more recommendations using a trained model trained with training data;
the limitation “wherein determining, by the cash flow forecast engine, the baseline cash flow forecast for the second time frame comprises:
assessing the financial data associated with the primary entity to determine predicted cash flow within the second time frame” encompasses no more than generically invoking a processor to apply the Judicial Exception step of assessing the financial data to determine the predicted capital surplus or deficit;
the limitation “determining a trend component based on the financial data associated with the primary entity for that time frame, wherein the trend component is indicative of a trend in the historical transactional data and wherein the trend component comprises a trend model” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining the trend component;
the limitation “removing the trend component from the financial data associated with the primary entity for that time frame to provide de-trended financial data” encompasses no more than generically invoking a processor to apply the Judicial Exception step of removing the trend component from the financial data;
the limitation “determining a second trend component based on the financial data associated with the primary entity for that second time frame, wherein the second trend component is indicative of a trend in the financial data associated with the primary entity and wherein the second trend component comprises a second trend model” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining the first seasonality coefficient;
the limitation “using the second trend model and the one or more second seasonality coefficient to determine the baseline cash flow forecast;
wherein the one or more second recommendations generated by the recommendation engine comprise recommendations to adjust the financial data associated with the primary entity to manage the predicted cash flow during the one or more second sub- time frames” encompasses no more than generically invoking a processor to apply the Judicial Exception step of using the mathematical model and the seasonality coefficient to the time frame to determine baseline cash flow forecast;
the limitation “generating, by the capital management platform, a cash flow forecast tool within a graphical user interface (GUI) of a display device, wherein the cash flow forecast tool depicts a representation of the baseline cash flow forecast and the one or more second recommendations” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating a cash flow forecast and depicting the representation of the baseline cash flow forecast and the one or more recommendations;
the limitation “in response to detecting user selection of one or more of the one or more recommendations using the cash flow forecast tool, adjusting the financial data associated with the primary entity in accordance with the selected one or more suggestions;
determining a modified cash flow forecast for the second time frame based on the adjusted financial data; and
depicting a representation of the modified cash flow forecast within the cash flow forecast tool” encompasses no more than generically invoking a processor to apply the Judicial Exception step of adjusting financial data in accordance with the user selection of suggestions, determining a modified cash flow forecast based on the adjustment;
the limitation “wherein determining, by the cash flow forecast engine, the baseline cash flow forecast comprises: determining the second seasonality component based on a first periodicity in the financial data associated with the primary entity; determining a fitness metric by comparing the financial data associated with the primary entity with the second trend model and the second seasonality component; responsive to the determined fitness metric being below a predetermined fitness threshold, determining a third seasonality component based on a second periodicity in the financial data associated with the primary entity; and projecting the second trend component and the third seasonality component to the second time frame to determine the baseline cash flow forecast” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining the second seasonality, determining the fitness metric, determining a third seasonality component if the metric is below the threshold and projecting the components to the time frame;
the limitation “predicting, by the cash flow forecast engine, a probability of receiving payment of each invoice having a due date in the second time frame and determining a modified cash flow forecast for the second time frame based on the predictions.
wherein predicting a probability of receiving payment of each invoice having a due date in the second time frame comprises determining an action score for a secondary entity associated with each invoice, wherein the action score is based on historic payment related data of the respective secondary entity, and determining the probability of the second entity paying the invoice within a given payment period, wherein the given period falls within the second time frame” encompasses no more than generically invoking a processor to apply the Judicial Exception step of predicting the probability of receiving payment of each invoice by determining an action score and determining the probability of the second entity paying the invoice within a given payment period;
the limitation “wherein the one or more recommendations comprise recommendations to adjust the financial data to manage the predicted cash flow during the one or more sub-time frames” encompasses no more than generically invoking a processor to apply the Judicial Exception step of providing the recommendations to adjust the financial data;
the limitation “wherein adjusting financial data comprises modifying a due date for payment on an invoice” encompasses no more than generically invoking a processor to apply the Judicial Exception step of modifying a due date for payment on the invoice;
the limitation “wherein the recommendations include one or more of seeking early payment of an outstanding invoice, seeking an extended term for payment of an outstanding invoice, subscribing to a financial product, varying terms of at least one of the one or more transactions” encompasses no more than generically invoking a processor to apply the Judicial Exception step of providing the recommendations;
the limitation “automatically executing, by the recommendation engine, at least one of the one or more second recommendations based on the target parameters” encompasses no more than generically invoking a processor to apply the Judicial Exception step of executing the at least one recommendations based the target parameters;
the limitation “analysing, by a retrospective training engine of the capital management platform, the automatically executed one or more second recommendations to determine whether the executed second recommendations mitigated the forecasted one or more cash flow shortfall periods or one or more cash flow excess periods; and revising the recommendation engine based on the analysis” encompasses no more than generically invoking a processor to apply the Judicial Exception step of analyzing the executed recommendations to determine whether the executed recommendation mitigated the forecasted shortfall;
the limitation “wherein the recommendation engine comprises one or more machine learning models and the revising the recommendation engine comprises training the machine learning model based on the analysis” encompasses no more than generically invoking a processor to apply the Judicial Exception step of training the machine learning model.
Other than being generally linked to the steps of the Judicial Exception, the additional elements in the above step(s) is/are recited at a high-level of generality, without technological detail of how the particular steps are performed technologically.
The additional element(s) of “memory” and/or “non-transitory storage medium” are generically recited to store data and/or instructions of the Judicial Exception.
The additional element(s) of “engine” or “platform” are generically recited to perform steps described only by a result-oriented solution with insufficient detail for how the engine accomplish it.
The examiner noted the generically recited additional elements are mere instructions to implement the Judicial Exception idea on a computer. Indeed, the instant claims (1) attempted to cover a solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result; (2) used of a computer or other machinery in its ordinary capacity for economic or other tasks or simply added a general purpose computer or computer components after the fact to the Judicial Exception and (3) generally applied the Judicial Exception to a generic computing environment without limitation indicative of practical application (See MPEP 2106.04(d)I). Thus, the claims are no more than Mere Instruction to Apply the Judicial Exception (See MPEP 2106.05(f)) or adding insignificant extra-solution activity to the judicial exception (See MPEP 2106.05(g)), which do not integrate the cited Judicial Exception into practical application (Step 2A prong two: No) The claims are directed to a Judicial Exception.
The claim does 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 element of using a processor to provide financial advice amounts 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. Dependent claim 2, 8, 12 and 15 merely limit the abstract idea but do not recite any additional element beyond the cited abstract idea, thus, do not amount to significantly more. No additional element currently recited in the claims amount the claims to be significantly more than the cited abstract idea. (Step 2B: No)
Therefore, claims 1, 2, 4-8, 12-18, 21 and 41 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed on 12/16/2025 have been fully considered but they are not persuasive.
Regarding the applicant’s argument that the Claims provide an improvement to the machine learning model similar to that of Ex parte Desjardins, the examiner respectfully disagrees. As noted in the previous response, the disclosure of the instant application does not specify what particular type of machine learning model is used in the instant invention, let alone the specific training of unspecified ML model. The examiner further noted that the approach provided by the instant claims is “determine a baseline cash flow forecast could be better information by taking into account a trend component and seasonality component”, which is not even nominally related to the technology of machine learning. Indeed, the alleged improvement is directed to the aspect of improve based line cashflow forecast, not to machine learning. It must be further noted that the mere inclusion of machine learning model usage / training does not render an otherwise abstract claim patent eligible under 101. In Recentive Analytics, Inc. v. Fox Corp., the Federal Circuit held that "patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101." 2023-2437, slip op. at 18 (Fed. Cir. Apr. 18, 2025). The court specifically rejected the argument that requiring iterative training of a machine learning model creates patent eligibility, noting that "[i]terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning." Id. at 12. As the patentee in Recentive conceded, "'using a machine learning technique … necessarily includes [an] iterative training step.'" Id. The court further explained that "the requirements that the machine learning model be 'iteratively trained' or dynamically adjusted . . . do not represent a technological improvement" because these features are inherent to the applying of machine learning technology itself. Id. Accordingly, the claimed invention here, which similarly applies conventional machine learning technique[s] to provide financial advice, fails to recite patent-eligible subject matter under 101.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHO KWONG whose telephone number is (571)270-7955. The examiner can normally be reached 9am - 5pm EST M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MICHAEL W ANDERSON can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHO YIU KWONG/Primary Examiner, Art Unit 3693