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 .
DETAILED ACTION
This is a non-final rejection. Claims 1-3, 6-9, 12-15, and 18 are pending.
Status of Claims
Applicant’s response date 02/27/2026. Amending claims 1, 5, 7, 12, 13, and 18.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/27/2026 has been entered.
Response to Amendment
The previously pending rejection under 35 USC 101, will be maintained. The 101 rejection is updated in light of the amendments.
With regard to the rejection under 35 USC 103- No art rejection has been put forth in the rejection for the reason found in the “Allowable Subject Matter” section found below. See applicant remarks pages 12-14 09/30/2025.
Response to Arguments
Applicant's arguments filed 02/27/2026 have been fully considered but they are not persuasive.
Response to Arguments under 35 USC 101:
Applicant argues (Pages 15-17 of the remarks):
These limitations define a structured processing framework that specifies how
historical cash flow data is segmented, how derived datasets are generated through
permutations and combinations, how weightages are computed for each day, and how
forecast cash flow data generated at a first time level is converted into a shorter second
time level using identified historical distribution patterns and computed distribution
weightages.
The claim therefore does not recite a fundamental economic practice in the abstract.
Instead, it recites a specific machine learning based computing method that defines the
manner in which forecast cash flow data is produced and converted across time levels.
Thus, the claims do not recite an abstract idea (Prong 1, Step 2A: NO).
Examiner respectfully disagrees:
With regard to an abstract idea, Independent Claims the claim, when “taken as a whole,” are directed to the abstract idea and substantially recite the limitations:
A machine learning based (ML-based) computing method for forecasting financial transactions based on sparse data, the ML-based computing method comprising:
receiving, by one or more hardware processors, one or more inputs from one or more users, wherein the one or more inputs comprise information related to at least one of: a forecast period and an entity;
generating, by the one or more hardware processors, cash flow data comprising at least one of: historical cash flow data and real-time cash flow data, based on the one or more inputs received from the one or more users;
determining, by the one or more hardware processors, a growth factor in at least one of: a week level and a month level based on at least one of: the historical cash flow data and the forecast period;
determining, by the one or more hardware processors, average cash flow data in at least one of: a week level and a month level based on at least one of: the historical cash flow data, the forecast period, a lookback period received from the one or more users, and the determined growth factor;
generating, by the one or more hardware processors, forecast cash flow data at a first time level for the forecast period using a machine learning model wherein the machine learning model comprises a regression-based machine learning model, and wherein generating the forecast cash flow data, using the regression-based machine learning model, comprises:
dynamically assigning, by the one or more hardware processors, weightages to the average cash flow data and the growth factor, using the regression-based machine learning model, by:
segmenting, by the one or more hardware processors, the historical cash flow data based on at least one of: a geographic location, an industry, a business segment, a legal entity, a type of transactions, a payment method, a product, a service, a customer, a sales channel, time and a currency;
generating, by the one or more hardware processors, a growth factor dataset by computing the growth factor for a plurality of permutations and combinations of the historical cash flow data and the forecast period:
generating, by the one or more hardware processors, an average cash flow dataset by computing the average cash flow data for a plurality of permutations and combinations of the historical cash flow data, the forecast period, and the growth factor: and
correlating, by the one or more hardware processors, the historical cash flow data with the generated growth factor dataset and the average cash flow dataset to compute the weightages to the average cash flow data and the growth factor for each day; and
multiplying, by the one or more hardware processors, the weighted average cash flow data and the weighted growth factor to generate the forecast cash flow data at the first time level for the forecast period inputted by the one or more users; and
converting, by the one or more hardware processors, the generated forecast cash flow data at the first time level into forecast cash flow data at a second time level using the regression-based machine learning model, wherein the first time level and the second time level comprise at least one of: a week level, a month level, a quarter level, and a year level, wherein the second time level is a shorter time period than the first time level, and wherein converting the generated forecast cash flow data comprises:
analyzing, by the one or more hardware processors, the historical cash flow data and holiday data associated with the forecast period to identify a historical distribution pattern;
computing, by the one or more hardware processors, a distribution weightage for each of a plurality of partial time segments within the second time level based on the identified historical distribution pattern; and
distributing, by the one or more hardware processors, the generated forecast cash flow data to the second time level by applying the computed distribution weightage to the generated forecast cash flow data;
providing, by the one or more hardware processors, an output of the forecast cash flow data associated with at least of the first time level and the second time level to the one or more users on a user interface associated with one or more electronic devices;
rending, by the one or more hardware processors, the output of the forecast cash flow data associated with at least one of the first time level and the second time level in a graphical format.
The Applicant's Specification titled "MACHINE LEARNING BASED (ML-BASED) COMPUTING METHOD AND SYSTEM FOR FORECASTING FINANCIAL TRANSACTIONS" emphasizes the business need for data analysis, "In summary, the present disclosure relates to generating forecast cash flow and providing an output of the forecast cast flow to the one or more users " (Spec. [0001]).
As the bolded claim limitations above demonstrate, independent claims 1, and 13 are recites the abstract idea of generating forecast cash flow and providing an output of the forecast cast flow to the one or more users. which is considered certain methods of organizing human activity because the bolded claim limitations pertain to (i) fundamental economic principles or practices and (ii) commercial or legal interactions. See MPEP §2106.04(a)(2)(II).
Applicant's claims as recited above provide a business solution of generating forecast cash flow and providing an output of the forecast cast flow to the one or more users. Applicant's claimed invention pertains to Certain methods of organizing human activity –fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations);” See MPEP §2106.04(a)(2)(II).
As the bolded claim limitations above demonstrate, independent claims 1, 7 and 13 are recites the abstract idea of weightages to the average cash flow data and the growth factor and multiplying the weighted average cash flow data and the weighted growth factor to generate cash flow data for the forecast period. which is considered Mathematical Concepts because the bolded claim limitations pertain to mathematical relationships and calculations. See MPEP §2106.04(a)(2)(II).
Applicant argues (Pages 17-20 of the remarks):
To overcome this inherent computational deficiency, independent Claims 1, 7, and 13
do not merely recite "forecasting". Instead, they recite a specific regression-based
machine learning architecture that forces pattern recognition through a specialized
pipeline. Specifically, the claims require dynamically assigning weightages by:
1. Segmenting historical cash flow data across specific dimensions;
2. Generating a growth factor dataset computing permutations and combinations
of the data;
3. Generating an average cash flow dataset computing further permutations and
combinations; and
4. Correlating the historical data with these specifically generated, multidimensional
datasets to compute daily weightages.
Furthermore, the claims recite a non-routine, hierarchical time-level conversion (e.g.,
month-level to week-level to day-level). This is not mere data display. Moreover, the
specification explains that this requires computing a plurality of weeks and a partial
week in both "backward" and "forward" directions to accurately distribute data across
irregular time segments.
Under McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016),
claims that recite a specific set of rules or a specific mathematical architecture that
improves the functioning of a computer-related technology are considered patent eligible.
The claims here do not preempt all financial forecasting; they claim a specific,
unconventional ML pipeline (permutation-based dataset generation, dynamic
regression weighting, and hierarchical time-conversion) that allows a computing
system to accurately process sparse data where conventional models
mathematically fail.
Therefore, the claims integrate the alleged abstract idea into a practical application
(Step 2A, Prong 2: YES).
The Examiner explicitly acknowledged that the prior art of record fails to teach the claimed combination of segmenting data, generating growth factor and average cash flow datasets through permutations, and correlating them to dynamically assign weightages using a regression-based machine learning model. Furthermore, the Examiner stated that "Upon further searching the examiner could not identify any prior art to teach these limitations."
Applicant respectfully submits that if an ordered combination of computational steps is entirely absent from the prior art and non-obvious to one of ordinary skill in the art, it fundamentally cannot be a "well-understood, routine, and conventional" activity
Examiner respectfully disagrees:
First, examiner point out with regard to applicant argument above that the references do not disclose a limitation in the claim. This argument is not persuasive because the test under Alice is not a matter of evidence but rather a test of law, the nonobviousness or novelty of those limitations would not provide an indication that those limitations are 'something more'. In other words, nonobviousness or novelty is not an indicia of eligibility - it is not an indicia that limitations provide "something more.
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional element, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use exception, such that it is more than a drafting effort designed to monopolize the exception.
The claims recite the additional limitation a machine learning model, processors, memory, subsystem, user interface, electronic devices, and a non-transitory are recited in a high level of generality and recited as performing generic computer functions routinely used in computer applications. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp. 134 S. Ct, at 2360,110 USPQ2d at 1984 (see MPEP 2106.05(f).
With regard to applicant argument to the machine learning. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general purpose computer; October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea. (“[M]erely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea”); 2019 Revised Guidance at 55. See also Trading Techs. Int’l, Inc. v. IBG LLC, 921 F.3d 1084, 1090 (Fed. Cir. 2019) (“This invention makes the trader faster and more efficient, not the computer. “[M]erely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea”); 2019 Revised Guidance at 55. See also Trading Techs. Int’l, Inc. v. IBG LLC, 921 F.3d 1084, 1090 (Fed. Cir. 2019) (“This invention makes the trader faster and more efficient, not the computer. This is not a technical solution to a technical problem.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (step 2A-prong two: NO).
The Alice framework, step 2B (Part 2 of Mayo) determine if the claim is sufficient to ensure that the claim amounts to “significantly more” than the abstract idea itself. These additional elements recite conventional computer components and conventional functions of:
Independent claims do not include my limitations amounting to significantly more than the abstract idea, along. The claims include various elements that are not directed to the abstract idea. These elements include a machine learning model, processors, memory, subsystem, user interface, electronic devices, and a non-transitory.
Examiner asserts that a machine learning model, processors, memory, subsystem, user interface, electronic devices, and a non-transitory are a generic computing element performing generic computing functions. (See MPEP 2106.05(f))
Further, with regard to mining (i.e., searching over a network), receiving, processing, storing data, and parsing (i.e. extract, transform data), the courts have recognized the following computer functions as well-understood, routing, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (i.e. “receiving, processing, transmitting, storing data”, etc.) are well-understood, routine, etc. (MPEP 2106.05(d))
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices.
In addition, [0145], of the specifications detail any combination of a generic computer system program to perform the method. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
Claim Rejections 35 USC §101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 6-9, 12-15, and 18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without a practical application or significantly more than the abstract idea.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Regarding Step 1
Claims 1-3, and 6 are directed to a method (process), claims 7-9, 12 are directed to a system (machine), and claims 13-15, and 18 are directed to a non-transitory (machine). Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1]
Claims 1-3, 6-9, 12-15, and 18 are directed toward the judicial exception of an abstract idea.
Independent claims 7, and 13 recites essentially the same abstract features as claim 1, thus are abstract for the same reason as claim 1.
Regarding independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1. A machine learning based (ML-based) computing method for forecasting financial transactions based on sparse data, the ML-based computing method comprising:
receiving, by one or more hardware processors, one or more inputs from one or more users, wherein the one or more inputs comprise information related to at least one of: a forecast period and an entity;
generating, by the one or more hardware processors, cash flow data comprising at least one of: historical cash flow data and real-time cash flow data, based on the one or more inputs received from the one or more users;
determining, by the one or more hardware processors, a growth factor in at least one of: a week level and a month level based on at least one of: the historical cash flow data and the forecast period;
determining, by the one or more hardware processors, average cash flow data in at least one of: a week level and a month level based on at least one of: the historical cash flow data, the forecast period, a lookback period received from the one or more users, and the determined growth factor;
generating, by the one or more hardware processors, forecast cash flow data at a first time level for the forecast period using a machine learning model wherein the machine learning model comprises a regression-based machine learning model, and wherein generating the forecast cash flow data, using the regression-based machine learning model, comprises:
dynamically assigning, by the one or more hardware processors, weightages to the average cash flow data and the growth factor, using the regression-based machine learning model, by:
segmenting, by the one or more hardware processors, the historical cash flow data based on at least one of: a geographic location, an industry, a business segment, a legal entity, a type of transactions, a payment method, a product, a service, a customer, a sales channel, time and a currency;
generating, by the one or more hardware processors, a growth factor dataset by computing the growth factor for a plurality of permutations and combinations of the historical cash flow data and the forecast period:
generating, by the one or more hardware processors, an average cash flow dataset by computing the average cash flow data for a plurality of permutations and combinations of the historical cash flow data, the forecast period, and the growth factor: and
correlating, by the one or more hardware processors, the historical cash flow data with the generated growth factor dataset and the average cash flow dataset to compute the weightages to the average cash flow data and the growth factor for each day; and
multiplying, by the one or more hardware processors, the weighted average cash flow data and the weighted growth factor to generate the forecast cash flow data at the first time level for the forecast period inputted by the one or more users; and
converting, by the one or more hardware processors, the generated forecast cash flow data at the first time level into forecast cash flow data at a second time level using the regression-based machine learning model, wherein the first time level and the second time level comprise at least one of: a week level, a month level, a quarter level, and a year level, wherein the second time level is a shorter time period than the first time level, and wherein converting the generated forecast cash flow data comprises:
analyzing, by the one or more hardware processors, the historical cash flow data and holiday data associated with the forecast period to identify a historical distribution pattern;
computing, by the one or more hardware processors, a distribution weightage for each of a plurality of partial time segments within the second time level based on the identified historical distribution pattern; and
distributing, by the one or more hardware processors, the generated forecast cash flow data to the second time level by applying the computed distribution weightage to the generated forecast cash flow data;
providing, by the one or more hardware processors, an output of the forecast cash flow data associated with at least of the first time level and the second time level to the one or more users on a user interface associated with one or more electronic devices;
rending, by the one or more hardware processors, the output of the forecast cash flow data associated with at least one of the first time level and the second time level in a graphical format.
The Applicant's Specification titled "MACHINE LEARNING BASED (ML-BASED) COMPUTING METHOD AND SYSTEM FOR FORECASTING FINANCIAL TRANSACTIONS" emphasizes the business need for data analysis, "In summary, the present disclosure relates to generating forecast cash flow and providing an output of the forecast cast flow to the one or more users " (Spec. [0001]).
As the bolded claim limitations above demonstrate, independent claims 1, and 13 are recites the abstract idea of generating forecast cash flow and providing an output of the forecast cast flow to the one or more users. which is considered certain methods of organizing human activity because the bolded claim limitations pertain to (i) fundamental economic principles or practices and (ii) commercial or legal interactions. See MPEP §2106.04(a)(2)(II).
Applicant's claims as recited above provide a business solution of generating forecast cash flow and providing an output of the forecast cast flow to the one or more users. Applicant's claimed invention pertains to Certain methods of organizing human activity –fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations);” See MPEP §2106.04(a)(2)(II).
As the bolded claim limitations above demonstrate, independent claims 1, 7 and 13 are recites the abstract idea of weightages to the average cash flow data and the growth factor and multiplying the weighted average cash flow data and the weighted growth factor to generate cash flow data for the forecast period. which is considered Mathematical Concepts because the bolded claim limitations pertain to mathematical relationships and calculations. See MPEP §2106.04(a)(2)(II).
Dependent claims 2-3, 6, 8-9, 12, 14-15, and 18 further reiterate the same abstract ideas with further embellishments (the bolded limitations), such as
claim 2 (Similarly claims 8 and 14) wherein the growth factor is determined by computing a ratio of the historical cash flow data for a selected week or month between previous years based on the forecast period inputted by the one or more users.
claim 3 (Similarly claims 9 and 15) wherein the average cash flow data are determined by multiplying the determined growth factor with average historical cash flow data computed for the selected week or month between the previous years.
claim 4 (Similarly claims 10 and 16) Cancelled
claim 5 (Similarly claims 11 and 17) Cancelled
claim 6 (Similarly claims 12 and 18) segmented based on at least one of:
the geographic location comprising at least one of: a country, a region, a state, a city, and a zip code of the city, the industry comprising at least one of: healthcare, retail, and technology, manufacturing and financial services; the business segment comprising at least one of: a product line, a geography, a customer group, and a service type; the legal entity comprising at least one of: a parent company, subsidiaries, joint ventures, and partnerships; the type of transaction comprising at least one of: purchase, sale, lease, rental, financing, and investment; the payment method comprising at least one of: a cash, a cheque, a credit card, and an electronic transfer, which tracks payment trends and manages a cash flow; the product or server comprising at least one of: a product line, a service line, a brand, a model, or a stock keeping unit (SKU);the customer comprising at least one of: a demographics, a behavior, buying patterns, preferences, and a customer lifetime value; the sales channel comprising at least one of: an online, a retail, a wholesale, direct and through intermediaries; the time comprising at least one of: a day, a week, a month, a quarter, a year, an hour and a minute; and the currency used in finance transactions.
which are nonetheless directed towards fundamentally the same abstract ideas as indicated for independent claims 1, 7 and 13.
Regarding Step 2A [prong 2]
Claims 1-3, 6-9, 12-15, and 18 fail to integrate the abstract idea into a practical application. Independent claims 1, 7 and 13 include the following additional elements which do not amount to a practical application:
Claim 1. A machine learning based (ML-based)
one or more hardware processors, a regression-based machine learning model,
a user interface associated with one or more electronic devices
Claim 7. A machine learning based (ML-based)
one or more hardware processors, memory, subsystems a regression-based machine learning model,
a user interface associated with one or more electronic devices
Claim 13. A non-transitory computer-readable storage medium
one or more hardware processors, a regression-based machine learning model,
a user interface associated with one or more electronic devices
The bolded limitations recited above in independent claims 1, 7 and 13 pertain to additional elements which merely provide an abstract-idea-based-solution implemented with computer hardware and software components, including the additional elements of a machine learning model, processors, memory, subsystem, user interface, electronic devices, and a non-transitory. which fail to integrate the abstract idea into a practical application because there are (1) no actual improvements to the functioning of a computer, (2) nor to any other technology or technical field, (3) nor do the claims apply the judicial exception with, or by use of, a particular machine, (4) nor do the claims provide a transformation or reduction of a particular article to a different state or thing, (5) nor provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of MPEP §2106.04(d)(1) and §2106.05 (a-c & e-h), (6) nor do the claims apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, in view of MPEP §2106.04(d)(2). The Specification provides a high level of generality regarding the additional elements claimed without sufficient detail or specific implementation structure so as to limit the abstract idea, for instance, ([0145]). Nothing in the Specification describes the specific operations recited in claims 1, 7 and 13 as particularly invoking any inventive programming, or requiring any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is somehow implemented using any specialized element other than all-purpose computer components to perform recited computer functions. The claimed invention is merely directed to utilizing computer technology as a tool for solving a business problem of data analytics. Nowhere in the Specification does the Applicant emphasize additional hardware and/or software elements which provide an actual improvement in computer functionality, or to a technology or technical field, other than using these elements as a computational tool to automate and perform the abstract idea. See MPEP §2106.05(a & e).
The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant's claimed invention which merely pertains to steps for generating forecast cash flow and providing an output of the forecast cast flow to the one or more users and the additional computer elements a tool to perform the abstract idea, and merely linking the use of the abstract idea to a particular technological environment. See MPEP §2106.04 and §21062106.05(f-h). Alternatively, the Office has long considered data gathering, analysis and data output to be insignificant extra-solution activity, and these additional elements do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.04 and §2106.05(g). Thus, the additional elements recited above fail to provide an actual improvement in computer functionality, or to a technology or technical field. See MPEP §2106.04(d)(1) and §2106§2106.05 (a & e).
Instead, the recited additional elements above, merely limit the invention to a technological environment in which the abstract concept identified above is implemented utilizing the computational tools provided by the additional elements to automate and perform the abstract idea, which is insufficient to provide a practical application since the additional elements do no more than generally link the use of the abstract idea to a particular technological environment. See MPEP §2106.04. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Alternatively, the Office has long considered data gathering and data processing as well as data output recruitment information on a social network to be insignificant extra-solution activity, and these additional elements used to gather and output recruitment information on a social network are insignificant extra-solution limitations that do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(g). The current invention generate forecast cash flow and providing an output of the forecast cast flow to the one or more users. When considered in combination, the claims do not amount to improvements of the functioning of a computer, or to any technology or technical field. Applicant's limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits.
Dependent claims 2-3, 6, 8-9, 12, 14-15, and 18 merely incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claims 1, 7 and 13 but, these features only serve to further limit the abstract idea of independent claims 1, 7 and 13. furthermore, merely using/applying in a computer environment such as merely using the computer as a tool to apply instructions of the abstract idea do nothing more than provide insignificant extra-solution activity since they amount to data gathering, analysis and outputting. Furthermore, they do not pertain to a technological problem being solved in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, and/or the limitations fail to achieve an actual improvement in computer functionality or improvement in specific technology other than using the computer as a tool to perform the abstract idea.
Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application.
Regarding Step 2B
Claims 1-3, 6-9, 12-15, and 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element(s) as described above with respect to Step 2A Prong 2, the additional element of claims 1, 7 and 13 include a machine learning model, processors, memory, subsystem, user interface, electronic devices, and a non-transitory. The displaying interface and storing data merely amount to a general purpose computer used to apply the abstract idea(s) (MPEP 2106.05(f)) and/or performs insignificant extra-solution activity, e.g. data retrieval and storage, as described above (MPEP 2106.05(g)) which are further merely well-understood, routine, and conventional activit(ies) as evidenced by MPEP 2106.06(05)(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser’s back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to generating forecast cash flow and providing an output of the forecast cast flow to the one or more users.
Claims 1-3, 6-9, 12-15, and 18 is accordingly rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Allowable Subject Matter
Claims 1-3, 6-9, 12-15, and 18 are objected to as being dependent upon a rejected based claim, but would be allowable if the independent claims were amended in such a way as to overcome the current rejection(s).
Regarding the 35 USC 103 rejection, No art rejections has been put forth in the rejection.
Closest prior art to the invention include Styles et al. US 2023/0306515: System and computer-implemented methods for capital management, Eder et al. US 2008/0071588: Method of and system for analyzing modeling and valuing elements of a business enterprise. Mozeika WO 2020/210709: Integrated personal finance management system for managing cash flow, and Karas, Michal, and Mária Režňáková. "Cash flows indicators in the prediction of financial distress." Engineering Economics 31.5 (2020): 525-535. None of the prior art of record, taken individually or in combination, teach, inter alia, teaches the claimed invention as detailed in independent claims, “wherein the machine learning model comprises a regression-based machine learning model, and wherein generating the forecast cash flow data, using the regression-based machine learning model, comprises: dynamically assigning, by the one or more hardware processors, weightages to the average cash flow data and the growth factor, using the regression-based machine learning model, by: segmenting, by the one or more hardware processors, the historical cash flow data based on at least one of: a geographic location, an industry, a business segment, a legal entity, a type of transactions, a payment method, a product, a service, a customer, a sales channel, time and a currency; generating, by the one or more hardware processors, a growth factor dataset by computing the growth factor for a plurality of permutations and combinations of the historical cash flow data and the forecast period: generating, by the one or more hardware processors, an average cash flow dataset by computing the average cash flow data for a plurality of permutations and combinations of the historical cash flow data, the forecast period, and the growth factor: and correlating, by the one or more hardware processors, the historical cash flow data with the generated growth factor dataset and the average cash flow dataset to compute the weightages to the average cash flow data and the growth factor for each day; and multiplying, by the one or more hardware processors, the weighted average cash flow data and the weighted growth factor to generate the forecast cash flow data at the first time level for the forecast period inputted by the one or more users;” The reason to withdraw the 35 USC 103 rejection of claims 1-3, 6-9, 12-15, and 18 in the instant application is because the prior art of record fails to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Upon further searching the examiner could not identify any prior art to teach these limitations. The prior art on record, alone or in combination, neither anticipates, reasonably teaches, not renders obvious the Applicant’s claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tripathi et al. US 2020/0410410: Computer-implemented method, system, and computer program product for automated forecasting.
Ramanathan CA 3117006: Systems and methods for quantum based optimization of stress testing.
Rabinovitch US 2020/0349640: Risk adjusted cash flow.
Mozeika WO 2020/210709: Integrated personal finance management system for managing cash flow.
Mozeika US 2020/0242699: Integrated personal finance management system for managing cash flow.
Novak et al. US 2014/0222669: Integrated electronic cash flow management system and method.
Yaplee et al. US 2015/0149333: Cash flow management.
Brereton et al. US 2015/0081483: Intraday cash flow optimization.
Karas, Michal, and Mária Režňáková. "Cash flows indicators in the prediction of financial distress." Engineering Economics 31.5 (2020): 525-535.
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/HAMZEH OBAID/Primary Examiner, Art Unit 3624