DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This office action is in response to Applicant’s communication filed March 16, 2023 in which claims 1-20 are pending in the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
2. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Examiner has identified independent system Claim 10 as the claim that represents the claimed invention for analysis and is similar to independent Claims 1 and 19.
The claims 1-9 are directed to a method, claims 10-18 are directed to a system and claim 19-20 is directed to a non-transitory computer-readable medium, which are one of the statutory categories of invention (Step 1: YES).
The claim 10 recites : a memory; and at least one processor coupled to the memory and configured to: receive first funds management data associated with an entity, the first funds management data corresponding to a current accounting period; generate input data for a machine learning (ML) model from the first funds management data, wherein the ML model is trained to predict whether a budget deficit or a budget surplus will exist for the entity at an end of the current accounting period, and wherein the ML model is trained based on training data generated from second funds management data associated with the entity, the second funds management data corresponding to one or more previous accounting periods; provide the input data as input to the ML model; and obtain, as an output from the ML model, a prediction of whether the budget deficit or the budget surplus will exist for the entity at the end of the current accounting period. These limitations (with the exception of italicized portions), under their broadest reasonable interpretation, when considered collectively as an ordered combination, is a process that covers Mental Processes as these limitations relate to concepts performed in the human mind (including an observation, evaluation, judgment, opinion and use of a pen and paper). Predicting whether a budget deficit or a budget surplus will exist for the entity at the end of the current accounting period can be performed in the human mind. In addition, the claims can also be classified under Mathematical concepts. Determining a budget deficit or surplus is a mathematical calculation. The claim also recites a memory, processor and a machine learning (ML) model which do not necessarily restrict the claim from reciting an abstract idea. That is, other than, a memory, processor and a machine learning (ML) model, nothing in the claim precludes the steps from being performed as a method of organizing human activity. If the claim limitations, under the broadest reasonable interpretation, covers the concepts that can be performed in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” and “Mathematical concepts” grouping of abstract ideas, respectively. Accordingly, the claim 10 recites an abstract idea (Step 2A: Prong 1: YES).
This judicial exception is not integrated into a practical application. The additional elements of a memory, processor and a machine learning (ML) model result in no more than simply applying the abstract idea using generic computer elements. The specification describes the additional elements of a memory, processor and a machine learning (ML) model to be generic computer elements (see Fig. 1, Fig. 4). Hence, the additional elements in the claim are generic components suitably programmed to perform their respective functions. The additional elements of a memory, processor and a machine learning (ML) model are recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computer arrangement. The presence of a generic computer arrangement is nothing more than mere instructions to implement the abstract idea on a computer (MPEP 2106.05(f)). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, the claims as a whole are not integrated into a practical application. Therefore, the claim 10 is directed to an abstract idea (Step 2A - Prong 2: NO).
The claim 10 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a memory, processor and a machine learning (ML) model are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). The additional elements, when considered separately and as an ordered combination, does not add significantly more (also known as an “inventive concept”) to the exception. The additional elements of the instant underlying process, when taken in combination, together do not offer significantly more than the sum of the functions of the elements when each is taken alone. Thus, claim 10 is not patent eligible (Step 2B: NO).
Similar arguments can he extended to other independent claims 1 and 19 and hence the claims 1 and 19 are rejected on similar grounds as claim 10. In addition, claim 1 also recites computers and claim 19 recites a non-transitory computer-readable device that amounts to generic computer implementation.
Dependent claims 2-9, 11-18 and 20 are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations only narrow the abstract idea further and thus correspond to “Mental Processes” and “Mathematical concepts” and hence are abstract for the reasons presented above. Dependent claims 2, 3, 4, 11, 12, 13 and 20 recite new additional elements that are not present in independent claims 1 or 10 or 19.
Claims 2 and 11 recite the additional elements of a supervised ML classifier. A supervised ML classifier, recited in the claims, is recited at a high level of generality and amounts to generic computer implementation. Hence, it does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea when considered individually and as an ordered combination.
Claims 3 and 12 recite the additional elements of a generalized linear model supervised ML classifier. A generalized linear model supervised ML classifier, recited in the claims, is recited at a high level of generality and amounts to generic computer implementation. Hence, it does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea when considered individually and as an ordered combination.
Claims 4, 13 and 20 recite the additional elements of a funds management system. A funds management system, recited in the claims, is recited at a high level of generality and amounts to generic computer implementation. Hence, it does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea when considered individually and as an ordered combination.
Viewing the claim limitations as a combination does not add anything further than looking at the claim limitations individually. When viewed either individually, or as a combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. Accordingly, claim(s) 1-20 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
3. Claims 1-3, 10-12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Peng et al., U.S. Patent Number (11,295,347 B1) in view of Shaaban et al., U.S. Patent Application Publication Number (2016/0042470 A1) and further in view of Geiger et al., U.S. Patent Application Publication Number (2022/0044277 A1).
Regarding Claim 1,
Peng teaches a computer-implemented method for implementing a machine-learning-based budget utilization predictor, comprising:
receiving first funds management data associated with an entity, the first funds management data corresponding to… (See at least Column 15, lines 46-53, “the training features 420 utilized to train the machine learning model 350 may comprise a plurality of quadruplet data sets 425, each of which comprises: (1) a time period 423 (e.g., an identifier corresponding to a particular week or other timeframe); (2) ad spend data 421 associated with the timeframe 423; (3) revenue data 422 associated with the time period 423; and (4) a weight 424 associated with the time period 423” Quadruplet data sets 425 can be interpreted as first funds management data);
generating input data for a machine learning (ML) model from the first funds management data, wherein the ML model is trained to predict …. (See at least Column 15, lines 53-59, “The quadruplet data sets 425 may then be input and processed by the machine learning model 350. The machine learning model 350 utilizes these training features to learn how to forecast predicted ROAS values 461 and predicted budget values 462 for the particular item 360 or items 360 which are the subject of the user's 305 forecast request.”);
providing the input data as input to the ML model (See at least Column 15, lines 53-55, “The quadruplet data sets 425 may then be input and processed by the machine learning model 350”);
obtaining, as an output from the ML model, a prediction of … (See at least Column 18, lines 3-18, “A machine learning cluster 570 may include a plurality of forecasters 571, each of which may represent an separate instance of the machine learning model 350 (FIGS. 3-4) that is trained to compute a predicted ROAS value 461 (FIG. 4) and/or a predicted budget value 462 (FIG. 4) pertaining to a forecast request 501. The forecasters 571 can be trained utilizing the data aggregated by the query cluster 560 to make these predictions. The notification cluster 580 can represent a distributed processing system that is configured to transmit notifications 581 to users 305 (FIG. 3) associated with the campaign predictions 355 (FIGS. 3-4) generated by the machine learning cluster 570.”);
wherein at least one of the receiving, generating, providing, and obtaining is performed by one or more computers (See at least Fig. 1, Fig. 2).
However, Peng does not explicitly teach,
current accounting period of which a budget deficit/surplus is determined.
wherein the ML model is trained based on training data generated from second funds management data associated with the entity, the second funds management data corresponding to one or more previous accounting periods;
obtaining a prediction of whether the budget deficit or the budget surplus will exist for the entity at the end of the current accounting period;
Shaaban, however, teaches,
current accounting period of which a budget deficit or surplus is determined.
obtaining a prediction of whether the budget deficit or the budget surplus will exist for the entity at the end of the current accounting period (See at least [0051], [0053], [0056], [0051], “The System may also create budget forecasts, which are used for custom financial plans. The System is initiated by a budget planner who enters historical data into the system or imports it from an accounting program or programs. This data may then be adjusted by the financial planner to make accurate predictions for the upcoming months and fiscal year.”
[0056], “Still another primary object of the present invention to provide an improved system and method for producing budgeting and cash flow forecasting to provide for a local company or firm a means to list all cash flow incoming items, and all expense items for the company, and predict the surplus or shortage for the company”. Making accurate budgeting predictions and predicting the surplus or shortage for the upcoming months and fiscal year can be interpreted as obtaining a prediction of whether the budget deficit or the budget surplus will exist for the entity at the end of the current accounting period.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the above-noted disclosure of Peng as it relates to implementing a machine learning based budget utilization predictor to incorporate the disclosure of Shaaban as it relates to providing budgeting and cash flow forecasting. The motivation for combining these references would have been to provide an improved system and method for producing budgeting and cash flow forecasting to provide for a firm a means to list all cash flow incoming items, and all expense items for the company, and predict the surplus or shortage for the company, which is then rolled up with other local companies or firms into a budget and cash flow forecast for a global company or firm having multiple local, regional, or national offices so that the global company or firm may determine if there will be a surplus which may be distributed to senior partners eligible to share in taking the surplus at year-end in the form of a distribution, dividend, or profit-sharing. (See at least Shaaban, [0056]).
However, the combination of Peng and Shaaban do not explicitly teach,
wherein the ML model is trained based on training data generated from second funds management data associated with the entity, the second funds management data corresponding to one or more previous accounting periods.
Geiger, however, teaches,
wherein the ML model is trained based on training data generated from second funds management data associated with the entity, the second funds management data corresponding to one or more previous accounting periods (See at least [0095], “In an embodiment, the hub computer system may include predictive models, which are trained using historical marketing information, geographic data, budget data, sales goals, performance data, and any other information provided to the hub computer system or any other information to which the hub computer system has access. After the predictive model is trained, it may be used to process geographic data, budget data, sales goals, performance data, and other data” Historical marketing information, geographic data, budget data, sales goals, performance data, etc serves as the second funds management data corresponding to one or more previous accounting periods);
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the above-noted disclosure of Peng and Shaaban to incorporate the disclosure of Geiger as it relates to generating a comprehensive marketing campaign. The motivation for combining these references would have been to generate a formula or recipe of marketing tasks needed to be performed to achieve a sales goal for a company. (See at least Geiger, [0095]).
Regarding Claim 2,
The combination of Peng, Shaaban and Geiger teaches the limitation of claim 1,
In addition, Peng teaches,
training the ML model based on the training data generated from the second funds management data, the training the ML model comprising: training a supervised ML classifier (See at least Column 12, lines 37-41, “the machine learning model 350 can be trained to perform these and other machine learning functions using any supervised, semi-supervised, and/or unsupervised training procedure”).
Regarding Claim 3,
The combination of Peng, Shaaban and Geiger teaches the limitation of claim 2,
In addition, Geiger teaches,
the training the supervised ML classifier comprising: training a generalized linear model supervised ML classifier (See at least [0097], “A predictive model might also refer to, but is not limited to, methods such as ordinary least squares regression, logistic regression, decision trees, neural networks, generalized linear models, and/or Bayesian models).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the above-noted disclosure of Peng and Shaaban to incorporate the disclosure of Geiger as it relates to generating a comprehensive marketing campaign. The motivation for combining these references would have been to understand relative factors contributing to an outcome, estimate unknown outcomes, discover trends, and/or make other estimations based on a data set of factors collected across prior experience such as results from prior marketing tasks and campaigns. (See at least Geiger, [0097]).
Regarding Claims 10 and 19,
Independent claims 10 and 19 are substantially similar to independent claim 1, and hence rejected on similar grounds. Claim 10 also recites a processor and a memory which is taught by Peng (see at least Fig. 2, Column 3, lines 34-37).
Regarding Claim 11,
claim 11 is substantially similar to claim 2, and hence rejected on similar grounds.
Regarding Claim 12,
Claim 12 is substantially similar to claim 3, and hence rejected on similar grounds.
Examiner’s Note
7. Examiner notes a search was performed but did not result in a prior art rejection against Claims 4-9, 13-18 and 20.
Examiner Request
8. The Applicant is request to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BHAVIN SHAH whose telephone number is (571)272-2981. The examiner can normally be reached on M-F 9AM-6PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Bennett Sigmond can be reached on 303-297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B.D.S./Examiner, Art Unit 3694
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694