DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/18/2025 has been entered.
Status of Claims
This is in reply to the claim amendments and remarks of the RCE filed 12/18/2025.
Claims 1, 10, and 19 have been amended.
Claims 1-5, 7-14, and 16-19 are currently pending and have been examined.
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 .
Response to Amendments
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1-5, 7-14, and 16-19, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims are not directed toward an abstract idea. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Examiner asserts that analyzing cashflows is a commercial interaction, where forecasting does not produce a tangible product, but rather data for a human to interpret (Organizing Human Activity). Applicant’s arguments are not persuasive.
The Examiner notes that using different pieces of data to perform the analysis does not improve the functioning of the computer or use the computer beyond generic use. The computer and Machine learning is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05). The Examiner further asserts the claims are not directed towards a Mental Process. The Examiner notes that the specific way the data is used to achieve the forecast is the abstract idea. Please see the rejection below. Applicant’s arguments are not persuasive.
Applicant argues the claims are related to McRO. The Examiner respectfully disagrees. The Examiner points to Page 2 of the McRO-Bascom Memo from December 2016, "The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation "that improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process." The Applicants’ claims are geared toward analyzing cash flows with specific rules and analysis steps for achieving a forecast for sales of commercial products, where these techniques are merely being applied/calculated in a computing environment. Simply applying these known concepts to a specific technical environment (e.g. the computers/Internet) does not account for significantly more than the abstract idea because it does not solve a problem rooted in computer technology nor does it improve the functioning of the computer itself because it is merely making a determination based on rules and/or mathematical relationships to output to a user. The Applicant’s claimed limitations do not appear to bring about any improvement in the operation or functioning of a computer per se, or to improve computer-related technology by allowing computer performance of a function not previously performable by a computer (see page 2 of the McRo-Bascom memo). The solution appears to be more of a business-driven solution rather than a technical one. In addition, McRO had no evidence that the process previously used by animators is the same as the process required by the claims. The Applicant’s claimed limitations and originally filed specification provide no evidence that the claimed process/functions are any different than what would be done without a computer, where there are no adjustments to the mental process to accommodate implementation by computers. Applicant’s arguments are not persuasive.
Applicant argues the claims are related to DDR. The Examiner respectfully disagrees. The Examiner again asserts that the claims are not directed towards a Mental Process. Please see the rejection below. The Examiner also asserts that the claimed “using a machine learning model, wherein the machine learning model comprises a regression-based machine learning model” is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant argues the claims are eligible under 2B. The Examiner respectfully disagrees. The Applicant does not point out what the additional elements are, but rather merely points to the machine learning mathematical relationships, which is abstract. Even if the machine learning was removed from the claims analyzing cash flows is still an abstract idea and generic recitation/use of a computer to implement would merely add the words apply it with the judicial exception (See MPEP 2106). The Examiner asserts that running forecast calculations on a general purpose computer does not improve the computer, but rather merely uses the computer as a tool for implementing the abstract idea. Applicant’s arguments are not persuasive.
Examiner note
The Examiner notes that the independent claims recite “a machine learning model” and “a machine learning algorithm”. Is there a difference between the model and the algorithm? The Examiner is just wondering the reason for this wording because both the model and the algorithm are recited at such a high level of generality they merely add the words apply it with the judicial exception.
Claim Objections
Claims 3-5 and 12-14 are objected: Claims 3 and 12 recite “wherein the machine learning model comprises a regression-based machine learning model”, which is already recited in the independent claim. Is this a new ML model? The Examiner recommends deleting the limitation to remove confusion.
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 1-5, 7-14, and 16-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1-5 and 7-9 are directed toward a process, claims 19 are directed toward a product, and claims 10-14 and 16-18 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a machine-learning based (ML-based) computing method for distributing financial transactions based on sparse data, the ML-based computing method comprising: receiving, by one or more hardware processors, one or more user inputs from one or more users, wherein the one or more user inputs comprise information related to at least one of: an entity and a lookback period; generating, by the one or more hardware processors, one or more data from the one or more inputs received from the one or more users, wherein the one or more data is characterized with sparse nature and comprises at least one of: holiday data, historical cash flow data, and forecast cash flow data, and wherein the forecast cash flow data comprise at least one of: month level forecast cash flow data and week level forecast cash flow data; converting, by the one or more hardware processors, the month level forecast cash flow data to the week level forecast cash flow data when at least one of: the holiday data, the historical cash flow data, and the month level forecast cash flow data are generated from the one or more inputs, using a machine learning model, wherein the machine learning model comprises a regression-based machine learning model for converting the month level forecast cash flow data to the week level forecast cash flow data, by: computing, by the one or more hardware processors, a plurality of weeks and a partial week of a month in at least one of: a backward direction and a forward direction, wherein the backward direction represents that the plurality of weeks and the partial week of the month are computed from a last day of the month going backward seven days. and wherein the forward direction represents that the plurality of weeks and the partial week of the month are computed from a first day of the month going forward seven days: generating, by the one or more hardware processors, a plurality of permutations of the plurality of weeks and the partial week of the month in the backward direction and the forward direction: obtaining, by the one or more hardware processors, the historical cash flow data and the holiday data for at least one of: each week and the partial week of the month for the entity inputted by the one or more users for a past lookback period; selecting, by the one or more hardware processors, a corresponding permutation of at least one of: each week and the partial week of the month based on the historical cash flow data using a machine learning algorithm; computing, by the one or more hardware processors, a plurality of weekly mapper value ratios for the selected permutation of at least one of: each week and the partial week of the month based on the historical cash flow data at the week level and the holiday data using the machine learning algorithm, wherein a default lookback period for computing the mapper value ratios is set to a predefined number of weeks, and wherein historical data from weeks containing a holiday is excluded from the computation of the mapper value ratios to avoid data skewness: and generating, by the one or more hardware processors, the week level forecast cash flow data by multiplying the month level forecast cash flow data with the computed weekly mapper value ratios for at least one of: each week and the partial week of the month; converting, by the one or more hardware processors, the week level forecast cash flow data to day level forecast cash flow data when at least one of: the holiday data, the historical cash flow data, and the week level forecast cash flow data are generated from the one or more inputs, using the machine learning model by applying a custom holiday forecast shifting algorithm, wherein the custom holiday forecast shifting algorithm is configured to: distribute the week level forecast cash flow data to an initial day level forecast based on a set of daily mapper value ratios derived from historical data; identify a holiday occurring on a specific day within a forecast week in the initial day level forecast; determine if the holiday lies on a predefined late-week day; in response to determining the holiday lies on the predefined late-week day, automatically move a first predetermined percentage of a forecast cash flow amount for the holiday to a previous business day within the same forecast week and prorate a remaining percentage of the forecast cash flow amount among a plurality of other business days of the same forecast week; in response to determining the holiday does not lie on the predefined late-week day, automatically move the first predetermined percentage of the forecast cash flow amount for the holiday to a next business day within the same forecast week and prorate the remaining percentage among the plurality of other business days of the same forecast week; and providing, by the one or more hardware processors, an output of at least one of the month level forecast cash flow data, the week level forecast cash flow data, and the day level forecast cash flow data to the one or more users on a user interface associated with one or more electronic devices (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing user input data regarding an entity and a lookback period to determine cashflow data and forecast cashflow data for certain time periods using machine learning to analyze specific percentages of forecasted cash flow and making adjustments to the forecast for certain holidays where, analyzing cash flows is a commercial interaction.
Dependent claims 2-5, 7-9, 11-14, and 16-18 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims additionally recite “a machine-learning based (ML-based) computing method; by one or more hardware processors; by the one or more hardware processors; using a machine learning model, wherein the machine learning model comprises a regression-based machine learning model; using the machine learning model; using a machine learning algorithm; using the machine learning algorithm; one or more users on a user interface associated with one or more electronic devices (claim 1)”; “machine learning based (ML-based) computing system for; he ML-based computing system comprising: one or more hardware processors; a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in the form of programmable instructions executable by the one or more hardware processors, and wherein the plurality of subsystems comprises: a data receiving subsystem configured to; a data generation subsystem configured to; using a machine learning model, wherein the machine learning model comprises a regression-based machine learning model; using a machine learning algorithm; using the machine learning algorithm; a forecast output subsystem configured to; one or more users on a user interface associated with one or more electronic devices (claim 10)”; “a non-transitory computer-readable storage medium having instructions stored therein that when executed by a hardware processor, cause the processor to execute operations of; using a machine learning model, wherein the machine learning model comprises a regression-based machine learning model; using a machine learning algorithm; using the machine learning algorithm; using the machine learning model; one or more users on a user interface associated with one or more electronic device (claim 19)”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-5, 7-9, 11-14, and 16-18 further narrow the abstract idea and recite no additional elements. The claims do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106). Further, method; System; and Product Independent claims 1, 10, and 19 recite “a machine-learning based (ML-based) computing method; by one or more hardware processors; by the one or more hardware processors; using a machine learning model, wherein the machine learning model comprises a regression-based machine learning model; using the machine learning model; using a machine learning algorithm; using the machine learning algorithm; one or more users on a user interface associated with one or more electronic devices (claim 1)”; “machine learning based (ML-based) computing system for; he ML-based computing system comprising: one or more hardware processors; a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in the form of programmable instructions executable by the one or more hardware processors, and wherein the plurality of subsystems comprises: a data receiving subsystem configured to; a data generation subsystem configured to; using a machine learning model, wherein the machine learning model comprises a regression-based machine learning model; using a machine learning algorithm; using the machine learning algorithm; a forecast output subsystem configured to; one or more users on a user interface associated with one or more electronic devices (claim 10)”; “a non-transitory computer-readable storage medium having instructions stored therein that when executed by a hardware processor, cause the processor to execute operations of; using a machine learning model, wherein the machine learning model comprises a regression-based machine learning model; using a machine learning algorithm; using the machine learning algorithm; using the machine learning model; one or more users on a user interface associated with one or more electronic device (claim 19)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0051-0052, 0057, and 0061-0062 and Figures 1. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-5, 7-9, 11-14, and 16-18 further narrow the abstract idea identified in the independent claims and present no additional elements that provide significantly more. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. The claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable over 35 USC 103
Claims 1-5, 7-14, and 16-19 are allowable over the prior art, but remain rejected under §101 for the reasons set forth above. Independent claims 1-5, 7-14, and 16-19 disclose a system and method for analyzing user input data regarding an entity and a lookback period to determine cashflow data and forecast cashflow data for certain time periods by computing partial weeks in a forward/backward direction and selecting corresponding permutations to weight and forecast cash flow by multiplying the month level forecast cashflow data with the weightage for the time period.
Regarding a possible 103 rejection: The closest prior art of record is:
Eder (US 2008/0071588 A1) – which discloses method and system for analyzing modeling and valuing elements of a business enterprise.
Turner et al. (US 2020/0279198 A1) – which discloses cash forecast system for analyzing cash flows.
Styles et al. (US 2023/0306515 A1) – which discloses systems for capital management.
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 1-5, 7-14, and 16-19, such as analyzing user input data regarding an entity and a lookback period to determine cashflow data and forecast cashflow data for certain time periods by computing partial weeks in a forward/backward direction and selecting corresponding permutations to weight and forecast cash flow by multiplying the month level forecast cashflow data with the weightage for the time period and determining forecasted cash flows and modifying the forecast accordingly. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight and the combination/arrangement of features are not found in analogous art. Specifically the claimed “a machine-learning based (ML-based) computing method for distributing financial transactions based on sparse data, the ML-based computing method comprising: receiving, by one or more hardware processors, one or more user inputs from one or more users, wherein the one or more user inputs comprise information related to at least one of: an entity and a lookback period; generating, by the one or more hardware processors, one or more data from the one or more inputs received from the one or more users, wherein the one or more data is characterized with sparse nature and comprises at least one of: holiday data, historical cash flow data, and forecast cash flow data, and wherein the forecast cash flow data comprise at least one of: month level forecast cash flow data and week level forecast cash flow data; converting, by the one or more hardware processors, the month level forecast cash flow data to the week level forecast cash flow data when at least one of: the holiday data, the historical cash flow data, and the month level forecast cash flow data are generated from the one or more inputs, using a machine learning model, wherein the machine learning model comprises a regression-based machine learning model for converting the month level forecast cash flow data to the week level forecast cash flow data, by: computing, by the one or more hardware processors, a plurality of weeks and a partial week of a month in at least one of: a backward direction and a forward direction, wherein the backward direction represents that the plurality of weeks and the partial week of the month are computed from a last day of the month going backward seven days. and wherein the forward direction represents that the plurality of weeks and the partial week of the month are computed from a first day of the month going forward seven days: generating, by the one or more hardware processors, a plurality of permutations of the plurality of weeks and the partial week of the month in the backward direction and the forward direction: obtaining, by the one or more hardware processors, the historical cash flow data and the holiday data for at least one of: each week and the partial week of the month for the entity inputted by the one or more users for a past lookback period; selecting, by the one or more hardware processors, a corresponding permutation of at least one of: each week and the partial week of the month based on the historical cash flow data using a machine learning algorithm; computing, by the one or more hardware processors, a plurality of weekly mapper value ratios for the selected permutation of at least one of: each week and the partial week of the month based on the historical cash flow data at the week level and the holiday data using the machine learning algorithm, wherein a default lookback period for computing the mapper value ratios is set to a predefined number of weeks, and wherein historical data from weeks containing a holiday is excluded from the computation of the mapper value ratios to avoid data skewness: and generating, by the one or more hardware processors, the week level forecast cash flow data by multiplying the month level forecast cash flow data with the computed weekly mapper value ratios for at least one of: each week and the partial week of the month; converting, by the one or more hardware processors, the week level forecast cash flow data to day level forecast cash flow data when at least one of: the holiday data, the historical cash flow data, and the week level forecast cash flow data are generated from the one or more inputs, using the machine learning model by applying a custom holiday forecast shifting algorithm, wherein the custom holiday forecast shifting algorithm is configured to: distribute the week level forecast cash flow data to an initial day level forecast based on a set of daily mapper value ratios derived from historical data; identify a holiday occurring on a specific day within a forecast week in the initial day level forecast; determine if the holiday lies on a predefined late-week day; in response to determining the holiday lies on the predefined late-week day, automatically move a first predetermined percentage of a forecast cash flow amount for the holiday to a previous business day within the same forecast week and prorate a remaining percentage of the forecast cash flow amount among a plurality of other business days of the same forecast week; in response to determining the holiday does not lie on the predefined late-week day, automatically move the first predetermined percentage of the forecast cash flow amount for the holiday to a next business day within the same forecast week and prorate the remaining percentage among the plurality of other business days of the same forecast week; and providing, by the one or more hardware processors, an output of at least one of the month level forecast cash flow data, the week level forecast cash flow data, and the day level forecast cash flow data to the one or more users on a user interface associated with one or more electronic devices (as required by claims 1-5, 7-14, and 16-19)”, thus rendering claims 1-5, 7-14, and 16-19 as allowable over the prior art.
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BRIAN EPSTEIN can be reached on (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
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