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 .
Status of the Application
Claims 1-2, 6-8, 12-14, 18-29 have been examined in this application.
The filling date of this application number recited above is 28 June 2024. Foreign priority has been claimed for IN-202411004607 in the Application Data Sheet, thus the examination will be undertaken in consideration of 23 January 2024, as the priority date, for applicable claims.
No additional information disclosure statement (IDS) has been filed to date.
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, 6-8, 12-14, 18-29 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. The Claims recite an abstract idea, Mental Process and/or Certain Methods of Organizing Human Activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
As per Claims 1, 7, and 13, the claims recite “a … method for report generation, the method comprising:
capturing, by one or more [people], a plurality of historical account data of a client account;
extracting, by the one or more [people], a plurality of item level features from the plurality of historical account data;
providing, by the one or more [people], the plurality of item level features and a set of user preferences to a natural language … model, [utilized] to identify patterns within the plurality of item level features and generate one or more client account reports based on the identified patterns and the set of user preferences; and
transmitting, to a user … by the one or more [people], the one or more client account reports;
generating, by the one or more [people], a set of liquidity rules for the client account based on the identified patterns, the set of liquidity rules generating a predicted balance for the account based on the identified patterns;
applying …, by the one or more [people], the set of liquidity rules to the client account; and
executing …, by the one or more [people] and on the client account, optimized transaction actions based on the applied set of liquidity rules.”
The limitation of the claims recited above, without considering the additional elements (e.g. computer, processor, etc.), under its broadest reasonable interpretation (BRI), recites Mental Processes. The method recited above is a process of capturing data, extracting information, using a model to identify information, providing the result data, generating rules, and applying rules. All these steps recited by the claims can be practically performed in the human mind, or by a human using a pen and paper. See MPEP 2106.04(III)(A):
“In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
• claims to "comparing BRCA sequences and determining the existence of alterations," where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014);
• a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011); and
• a claim to identifying head shape and applying hair designs, which is a process that can be practically performed in the human mind, In re Brown, 645 Fed. App'x 1014, 1016-17 (Fed. Cir. 2016) (non-precedential).”
Although the claim may recite using a computer to capture, extract, provide, generate, apply, execute, and transmit data, performing a mental process on a generic computer still recite a mental process. See MPEP 2106.04(III)(C):
“Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").”
Therefore, the claim recites an abstract idea, mental process.
Additionally, the limitation of the claim recited above, under BRI, recites Certain Methods of Organizing Human Activity, specifically under fundamental economic principles or practices. The method recited above is a process of analyzing data with respect to the financial account, which includes generating liquidity rules and applying the rules to execute the transaction, wherein the goal of the invention is to mitigate risk, as disclosed by Specification:
[0018] “Using the disclosed techniques, users (e.g., account owners, administrators, or managers) may optimize returns, minimize costs, and mitigate risks associated with both present and future cash positions”,
which is fundamental economic principles or practices. Therefore, the claim recites an abstract idea, certain methods of organizing human activity.
This judicial exception is not integrated into practical application. In particular, the claims recite an additional element of “computer”, “processor”, “interface”, “system”, “memory”, and “non-transitory computer-readable medium” to perform the method recited above by instructing the abstract idea to be performed “by” these generic computer components. As disclosed by Figure 6 and Specification:
[0049] “In general, any process or operation discussed in this disclosure that is understood to be computer-implementable, such as the processes illustrated in the flowcharts disclosed herein, may be performed by one or more processors of a computer system, such as any of the systems or devices in the exemplary environments disclosed herein, as described above”
[0054] “While the disclosed methods, devices, and systems are described with exemplary reference to transmitting data, it should be appreciated that the disclosed aspects may be applicable to any environment, such as a desktop or laptop computer, an automobile entertainment system, a home entertainment system, etc. Also, the disclosed aspects may be applicable to any type of Internet protocol”
the additional elements used for the method is generic computer system available to the public merely applied to perform its basic functionalities (e.g. capture, extract, provide, and transmit data), and does not require any specialized hardware or component to carry out the method. These general computer components are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer system. Merely using the generic computer system as a tool to perform the abstract idea (e.g. mere “apply it”) is not indicative of integration into a practical application; see MPEP 2106.05(f). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data, or merely reciting to perform actions “automatically”) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activities) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims also recite an additional element “natural language machine-learning model, trained to identify patterns …”. The model is merely applied as a black-box model to provide an input (e.g. the plurality of item level features and a set of user preferences) which gives an output (e.g. generate one or more client account reports). There is no technological improvement, modification, alterations, control, or changes upon the model itself or any of the underlying technology, wherein the model may be any “stored equation” which had been trained previously, and are merely applied to perform the abstract idea (e.g. provide input to give output). As similarly discussed above, mere “apply it” is not indicative of integration into a practical application. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, the additional element of using a computer based system is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer system. The claims lack sufficient technical details to provide how these limitations may provide technological steps or technical details on how it is particularly implemented on a computer to improve its system or any of its underlying hardware or components (e.g. how it is performed on the computer, how it could improve the computer itself, how it could manipulate the computer to function in a specific way other than its generic functionality, and/or how it could improve any of the underlying technology), but merely applies the generic computer system to perform its generic functionalities, such as capturing, extracting, providing, and transmitting data. Merely using the generic computer system as a tool to perform the abstract idea (e.g. mere “apply it”) is not indicative of an inventive concept (aka “significantly more”). In view of the Specification cited above, the judicial exception is not applied with or used by a particular machine. As held in Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 199 (1978) and Bancorp Services v. Sun Life, 687 F.3d 1266, 1276, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012), “the routine use of a computer to perform calculations cannot turn an otherwise ineligible mathematical formula or law of nature into patentable subject matter.” The claims are not patent eligible.
Regarding dependent claims, they are still directed to an abstract idea without significantly more.
Claims 2, 8, and 14 recite “wherein the natural language machine-learning model is an artificial intelligence model.” The claims specify the model as an AI model, but as similarly discussed above, the model is merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 6, 12, and 18 recite “further comprising: providing, by the one or more processors, the optimized transaction actions to a predictive machine-learning model trained to identify patterns within the optimized transaction actions and generate an updated predicted balance for the client account based on the identified patterns; and transmitting, to the user interface by the one or more processors, the updated predicted balance.” The claims provide further steps to provide data to the model and transmitting the result, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 19 recites “wherein the plurality of historical account data comprises at least one of a funds transfer, a purchase, an account credit, or a payment.” The claim provides further details regarding the data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 20 recites “wherein the plurality of item level features comprise numerical and/or textual data associated with the plurality of historical account data.” The claim provides further details regarding the data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 21, 24, and 27 recite “wherein the one or more client account reports describe the identified patterns in natural language.” The claims provide further details regarding the data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 22, 25, and 28 recite “wherein the one or more client account reports describe a set of steps or actions responsive to the identified patterns.” The claims provide further details regarding the data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 23, 26, and 29 recite “wherein the one or more client account reports describe how at least one circumstances affects the client account, in natural language.” The claims provide further details regarding the data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
These additional steps of each claims fail to remedy the deficiencies of their parent claim above because they are merely further limiting the rules used to conduct the previously recited abstract idea, and are therefore rejected for at least the same rationale as applied to their parent claim above.
Claims 2, 8, 6, 12, 14, and 18-29, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are sufficient to integrate into a practical application and do not amount to significantly more than the judicial exception. Similarly to the independent claims, each claim recites using a generic computer component to perform the abstract idea as mentioned above. Merely using the generic computer system as a tool to perform the abstract idea (e.g. “apply it”) is not indicative of an inventive concept (aka “significantly more”). Therefore, prong 2 and step 2B analysis are similar to above and these claims are not eligible.
Therefore, Claims 1-2, 6-8, 12-14, 18-29 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 7, 13, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Brereton et al. (US 20150081542 A1), in view of Malyack et al. (US 20190156253 A1), in view of Papadopoulos (US 20210109485 A1), and in view of Kotarinos (US 20220122181 A1).
As per claims 1, 7, and 13, Brereton teaches a computer-implemented method for report generation, the method comprising:
capturing, by one or more processors, a plurality of historical account data of a client account (See Figure 2 – block 204, as disclosed [0027] “At block 204, customer profile data 134 associated with the customer is accessed (e.g., from the customer profile database 114), historical transaction data 126 is accessed, and historical market data (e.g., from external data sources 112)”);
extracting, by the one or more processors, a plurality of item level features from the plurality of historical account data ([0020] “The historical values of data associated with the transactions 116 are shown in FIG. 1 as historical transaction data 126, which may be used by the offline model learning engines 104 for identifying patterns to produce model parameters 128. Transaction history includes historical information about the transactions conducted between the customer and the bank and/or other enterprises. For example, transaction history data may include frequency of transactions, a frequency and dates of transactions involving a customer's daily limits (cash or credit), exception data, any defaults that may have occurred and the dates of the defaults, and average dollar amount of transactions over a period of time”);
providing, by the one or more processors, the plurality of item level features and a set of [information] to a natural language machine-learning model, trained to identify patterns within the plurality of item level features and generate one or more client account reports based on the identified patterns and the set of [information] (See Figure 2 – blocks 206 and 208, as disclosed [0027] “In an embodiment, at block 206, the offline model learning engine 104 inputs the customer profile data 134, the historical transaction data 126 and the historical market data and generates model parameters 128” and [0029] “Once the model parameters 128 are estimated (e.g., learned) by the offline model learning engine 104, block 208 is performed. At block 208, the TRDL assessment analytics engine 106 is executed to generate a suggested TRDL using both the model parameters 128 and real-time market information retrieved as external information 132 from the external sources 112 as input”); and
transmitting, to a user interface by the one or more processors, the one or more client account reports (See Figure 2 – block 210, as disclosed [0029] “At block 210, the suggested TRDL is output”).
Although Brereton teaches the historical transaction data comprising various data values ([0020]) which is used as input to the model learning engine, the prior art does not seem to explicitly disclose that the data values are “extracted” from the data. However, Malyack teaches:
capturing, by one or more processors, a plurality of historical account data of a client account (See Figure 6 – block 603, as disclosed [0115] “At block 603, the volume forecasting engine accesses historical data (e.g., historical package manifest information) to generate a historical data set for one or more historical volume forecast. In some embodiments, “historical data” may be or include any data that was received and/or analyzed prior to the receiving of the data at block 601”);
extracting, by the one or more processors, a plurality of item level features from the plurality of historical account data (See Figure 6 – block 604, as disclosed [0115] “At block 604, the volume forecasting engine extracts one or more features from the historical data set”);
providing, by the one or more processors, the plurality of item level features and a set of [additional data] to a natural language machine-learning model … ([0115] “In some embodiments, the volume forecasting engine modifies the volume forecast learning model by reading inputs from an operator or a learning model analyzing the difference between the one or more features extracted from the additional volume forecast data and the one or more features extracted from the historical data set”);
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize extracting features from the historical data set, which is used as inputs to the machine learning model, as in Malyack in the system executing the method of Brereton, wherein Brereton already teaches of providing historical data as inputs to the machine learning model, with the motivation of offering to [0024] improve the accuracy in forecast and [0025] improve existing software technologies by automating tasks as taught by Malyack over that of Brereton.
Although Brereton teaches of providing various inputs to the machine learning model to generate an output (Figure 2 – blocks 206 to 210), the prior art does not seem to explicitly disclose that the machine learning model utilizes “a set of user preferences”. However, Papadopoulos teaches:
providing, by the one or more processors, the plurality of item level features and a set of user preferences to a natural language machine-learning model, trained to identify patterns within the plurality of item level features and generate one or more client account reports based on the identified patterns and the set of user preferences (See Figure 9 displaying an interface comprising various user preferences (e.g. settings) used for the machine learning model, as disclosed [0145] “The user may assist in the training of the learning models of semantic application system 506 by selecting options from settings 906. The user may view various live and historical data time series to input into the learning model to be tagged with a semantic data tag. The live and historical data may be retrieved from data source 904”);
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize user settings for the machine learning model as in Papadopoulos in the system executing the method of Brereton, wherein Brereton already teaches of utilizing a machine learning model, with the motivation of offering to improve user experience and convenience by allowing user to update the settings and [0181] “further improve the accuracy of the machine learning models” as taught by Papadopoulos over that of Brereton.
Brereton may not explicitly disclose, but Kotarinos teaches:
generating, by the one or more processors, a set of liquidity rules for the client account based on the identified patterns, the set of liquidity rules generating a predicted balance for the account based on the identified patterns ([0057] “In this step, the client first picks one of the potential portfolios, which is run in real-time through a big data analytics process involving machine learning and time series analysis … The portfolio manager can swap out the selected portfolio for another portfolio, and then compare yet again to the client's current assets or against potential portfolios. By using this sophisticated real-time analytics-driven system, portfolio managers can convey complex liquidity issues to their clients and understand the different kinds of liquidity risks that can occur at different periods in time” wherein time series analysis uses historical data to identify patterns (e.g. relationship over time), as disclosed [0042] “Time series analysis relates to a field of analysis that focuses on panel data. Panel data refers to data that has a relationship over a time dimension. For example, the daily trading prices of a stock have a relationship over time such that the previous day's closing price is more indicative of the trajectory of a stock than the closing price a week ago, and the closing price a week ago is more indicative of a stock's trajectory than the closing price a month ago”);
applying automatically, by the one or more processors, the set of liquidity rules to the client account ([0058] “Once a portfolio is chosen, the final step involves rebalancing the client's current portfolio as shown in FIG. 8 to the portfolio he or she chose in step 9. During this process, the portfolio is run through a rebalancing algorithm that uses optimization theory and measure theory to decide what the biggest allocation issues are in the client's current asset allocation. The current allocations are compared using techniques from optimization and measure theory to determine how to rebalance the portfolio. The client is then presented with a series of rebalancing steps in order of severity, showing what asset or collection of assets to short from his or her current portfolio (sell) and what asset or collection of assets to long (buy) to get closer to the chosen allocation. In addition to being presented in order of severity, the steps are also classified based on how urgently these actions should be taken”); and
executing automatically, by the one or more processors and on the client account, optimized transaction actions based on the applied set of liquidity rules ([0058] “The result is that the portfolio manager will then be able to, over time, reposition the client's portfolio as market conditions make for favorable restructuring conditions. This technique allows the portfolio manager to systematically move assets for the client's benefit while understanding the implications of each of these rebalancing decisions”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the steps to generate, apply, and execute the liquidity rules for optimized transaction actions for the client account as in Kotarinos in the system executing the method of Brereton, with the motivation of offering to provide [0017] “an advance in the characterization and study of liquidity and addresses a client's propensity for liquidity and risk” as taught by Kotarinos over that of Brereton.
As per claim 19, Brereton teaches the non-transitory computer-readable medium of claim 13, wherein the plurality of historical account data comprises at least one of a funds transfer, a purchase, an account credit, or a payment ([0020] “Transaction history includes historical information about the transactions conducted between the customer and the bank and/or other enterprises”).
As per claim 20, Brereton may not explicitly disclose, but Malyack teaches the non-transitory computer-readable medium of claim 13, wherein the plurality of item level features comprise numerical and/or textual data associated with the plurality of historical account data ([0100] “In some embodiments, extracting features at block 402 includes generating a mapping of some or each of the received volume forecast data to one or more classes. The generating of the mapping may include utilizing one or more data structures (e.g., a hash table) and/or learning models (e.g., a word embedding vector model). For example, in some embodiments, some or each of the volume forecast data is run through a word embedding vector model (e.g., WORD2VEC)”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the extracting data as textual data as in Malyack in the system executing the method of Brereton, with the motivation of offering to [0024] improve the accuracy in forecast and [0025] improve existing software technologies by automating tasks as taught by Malyack over that of Brereton.
Claims 2, 8, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Brereton, in view of Malyack, in view of Papadopoulos, in view of Kotarinos, and in view of Muse (US 20220059230 A1).
As per claims 2, 8, and 14, Brereton may not explicitly disclose, but Muse teaches the computer-implemented method of claim 1, the system of claim 7, and the non-transitory computer-readable medium of claim 13, wherein the natural language machine-learning model is an artificial intelligence model ([0179] “Continuous learning can be used in machine learning environments to ensure an artificial intelligence deployment is continually updated as new data is collected. Such an implementation can address the limitations of a one-time training of the models 230, 315, 335, 355 and enable the models 230, 315, 335, 355 in various embodiments to continuously update themselves and thus become more accurate in generating predictions”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize artificial intelligence as in Muse in the system executing the method of Brereton, wherein Brereton already teaches of utilizing a machine learning model, with the motivation of offering to [0179] “continuously update themselves and thus become more accurate in generating predictions … lead to more accurate models” as taught by Muse over that of Brereton.
Claims 6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Brereton, in view of Malyack, in view of Papadopoulos, in view of Kotarinos, and in view of MAITRA et al. (US 20200402156 A1).
As per claims 6 and 12, Brereton may not explicitly disclose, but MAITRA teaches the computer-implemented method of claim 1, and the system of claim 7, further comprising:
providing, by the one or more processors, the optimized transaction actions to a predictive machine-learning model trained to identify patterns within the optimized transaction actions and generate an updated predicted balance for the client account based on the identified patterns ([0012] “In such a case, the financial institution may employ a forecasting model to predict a future balance of an account. The forecasting model may be used to make a prediction of a future balance of each account maintained by the financial institution, or in some cases, a separate forecasting model may be used for each account maintained by the financial institution” or see also [0050] “In some implementations, the liquidity management platform, or another device, may train the prediction model to predict an account balance of an account based on features relating to a behavioral pattern of the account, such as quantitative features and/or spatial features, as described above”); and
transmitting, to the user interface by the one or more processors, the updated predicted balance ([0059] “In some implementations, the liquidity management platform may be configured with the set of rules. In such a case, after determining account balance predictions, the liquidity management platform may determine a liquidity buffer for the entity based on the account balance predictions and the set of rules. In some cases, the liquidity management platform may transmit a notification to a user device of the entity that identifies the determined liquidity buffer”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize predicted balance as in MAITRA in the system executing the method of Brereton, wherein Brereton already teaches of utilizing a machine learning model with the input to provide an output, with the motivation of offering to provide [0060] “improved accuracy and efficiency, thereby conserving resources and permitting a financial institution to operate with improved efficiency” as taught by MAITRA over that of Brereton.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Brereton, in view of Malyack, in view of Papadopoulos, in view of Kotarinos, in view of Muse, and in view of MAITRA.
As per claim 18, Brereton may not explicitly disclose, but MAITRA teaches the non-transitory computer-readable medium of claim 14, the operations further comprising comprising:
providing, by the one or more processors, the optimized transaction actions to a predictive machine-learning model trained to identify patterns within the optimized transaction actions and generate an updated predicted balance for the client account based on the identified patterns ([0012] “In such a case, the financial institution may employ a forecasting model to predict a future balance of an account. The forecasting model may be used to make a prediction of a future balance of each account maintained by the financial institution, or in some cases, a separate forecasting model may be used for each account maintained by the financial institution” or see also [0050] “In some implementations, the liquidity management platform, or another device, may train the prediction model to predict an account balance of an account based on features relating to a behavioral pattern of the account, such as quantitative features and/or spatial features, as described above”); and
transmitting, to the user interface by the one or more processors, the updated predicted balance ([0059] “In some implementations, the liquidity management platform may be configured with the set of rules. In such a case, after determining account balance predictions, the liquidity management platform may determine a liquidity buffer for the entity based on the account balance predictions and the set of rules. In some cases, the liquidity management platform may transmit a notification to a user device of the entity that identifies the determined liquidity buffer”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize predicted balance as in MAITRA in the system executing the method of Brereton, wherein Brereton already teaches of utilizing a machine learning model with the input to provide an output, with the motivation of offering to provide [0060] “improved accuracy and efficiency, thereby conserving resources and permitting a financial institution to operate with improved efficiency” as taught by MAITRA over that of Brereton.
Claims 21, 24, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Brereton, in view of Malyack, in view of Papadopoulos, in view of Kotarinos, and in view of Calzaretta et al. (US 20180276684 A1).
As per Claims 21, 24, and 27, Brereton may not explicitly disclose, but Calzaretta teaches the computer-implemented method of claim 1, the system of claim 7, and the non-transitory computer-readable medium of claim 13, wherein the one or more client account reports describe the identified patterns in natural language ([0003] “According to embodiments, a method includes obtaining a pattern that describes an event in a natural language format. The pattern that describes the event is converted into at least one identified pattern key value pair”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize describing the pattern in a natural language format as in Calzaretta in the system executing the method of Brereton, with the motivation of offering to [0011] improve accuracy in pattern matching as taught by Calzaretta over that of Brereton.
Claims 22, 25, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Brereton, in view of Malyack, in view of Papadopoulos, in view of Kotarinos, and in view of Dubost et al. (US 20160283584 A1).
As per Claims 22, 25, and 28, Brereton may not explicitly disclose, but Dubost teaches the computer-implemented method of claim 1, the system of claim 7, and the non-transitory computer-readable medium of claim 13, wherein the one or more client account reports describe a set of steps or actions responsive to the identified patterns ([0034] “Additionally, or alternatively, embodiments of the present disclosure may output a report that recommends or suggests various functions and/or optimizations for the DBA to perform based on the analysis of the identified patterns” or also [0071] “By way of example, control computer 200 may generate report 50 and/or a recommendation based on the command pattern data, and output the report 50 and/or recommendation to a display or other device for the user (box 82)” or see also [0105] “Memory 204 stores programs and instructions, such as the control program 210 previously mentioned, that cause the processing circuit 202 to retrieve the transaction log 40, analyze the transaction entries in the transaction log 40, identify patterns of SQL queries 42 based on that analysis, and output that information in a generated report that may include recommendations to a user, as previously described”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize report including recommendations based on the identified patterns as in Dubost in the system executing the method of Brereton, with the motivation of offering to [0002-0005] improve transaction log database management and business applications as taught by Dubost over that of Brereton.
Claims 23, 26, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Brereton, in view of Malyack, in view of Papadopoulos, in view of Kotarinos, and in view of Opedal (US 20240362640 A1).
As per Claims 23, 26, and 29, Brereton may not explicitly disclose, but Opedal teaches the computer-implemented method of claim 1, the system of claim 7, and the non-transitory computer-readable medium of claim 13, wherein the one or more client account reports describe how at least one circumstances affects the client account, in natural language ([0053] “In some implementations, the features correspond to a first set of feature types that were identified based on natural language analyst-generated reports regarding whether the transactions in the training set were fraudulent. The fraud detection model, when applied to the received transaction data, may be configured to output a first assessment regarding whether the requested transaction is fraudulent”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize report with natural language determining fraudulent transactions as in Opedal in the system executing the method of Brereton, with the motivation of offering to [0014] improve the model and improve fraud detection as taught by Opedal over that of Brereton.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-2, 6-8, 12-14, 18-29 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over the claims of copending Application No. 18/758,360. Although the claims at issue are not explicitly identical, they are not patentably distinct from each other because they are both directed towards a system with a process comprising: capturing historical transaction data, extracting item level features from the historical transaction data, providing the features to a machine-learning model trained to identify patterns to generate an output, transmitting the output to a user interface, generating liquidity rules for the client account based on the identified patterns, applying the liquidity rules to the client account, and executing optimized transaction actions based on the applied liquidity rules. The only difference between the two copending applications is the output data from the machine learning model, which is “one or more client account reports” (18/758,330) vs. “set of liquidity rules” (18/758,360), which are also disclosed in dependent claim 4 from ‘330 application and dependent claim 5 from ‘360 application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Response to Arguments
Applicant's arguments, see pages 9 to 11, filed 10-October-2025, with respect to 35 U.S.C. 101 rejection have been fully considered but they are not persuasive. As discussed above under 35 U.S.C. 101 rejection, considering the claims without the additional elements (e.g. system, processor, etc.), the claims amendment with respect to the steps of generating liquidity rules based on identified patterns, applying the rules to the account, and executing transaction actions based on the applied rules is still part of the abstract idea (i.e. mental process and/or certain methods of organizing human activities), and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application. The current claim limitations do not associate these steps with the machine learning model, wherein the claims merely apply the machine learning model to output a client account report. Mere “apply it” is not “significantly more”. Therefore, the 35 U.S.C. 101 rejection is maintained.
Applicant’s arguments, see pages 11 to 13, with respect to 35 U.S.C. 103 rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments, see page 13, with respect to Double Patenting Rejection have been fully considered but they are not persuasive. As discussed above, although the claims at issue are not explicitly identical, they are not patentably distinct from each other because the both application disclose a system with similar claim limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
GULATI et al. (US 20210350437 A1) discloses [0047] “In response to identifying a pricing-related pattern recognized by the machine learning algorithm from the recognized pricing-related patterns that correspond to the received product notification request, the pricing-related pattern identified by the machine learning algorithm may be processed to determine whether the product and related merchant information is to be included in the product notification”;
Washam et al. (US 11410111 B1) discloses [Col 4 Lines 66-67 to Col 5 Lines 1-15] “Accordingly, a management team might provide, as input to the model, the business' actual current financial data (i.e., input metrics) corresponding to the type of input metrics that were used to train the machine learning model, and receive, as an output from the model, the output metric that the models were trained to predict, such as a corporate liquidity value. In such an example, the predicted liquidity value represents the liquidity value that would be expected from the business, based on the relationships between the input metrics and the output liquidity value metric that were identified during the machine learning process using the data about other businesses. If the business's current actual liquidity level is different than the predicted liquidity value, that might suggest to corporate management that its liquidity levels are inappropriate or not optimal (or at least that its liquidity levels are different than other similarly-situated businesses)”.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HENRY H JUNG/ Examiner, Art Unit 3695
/CHRISTINE M Tran/ Supervisory Patent Examiner, Art Unit 3695
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