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, 3, 5-9, 11, 13-17, and 19-20 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-202411004604 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, 3, 5-9, 11, 13-17, and 19-20 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 are directed to an abstract idea, Mental Process, Certain Methods of Organizing Human Activity and/or Mathematical Concepts. 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, 9, and 17, the claims recite “a … method for liquidity optimization, the method comprising:
capturing, by one or more [people], a plurality of historical transaction data of a client account;
extracting, by the one or more [people], a plurality of item level features from the plurality of historical transaction data;
providing, by the one or more [people], the plurality of item level features to a generative … model [utilized] to identify patterns within the plurality of item level features and generate a set of liquidity rules for the client account based on the identified patterns, the plurality of patterns including a change in a balance of the client account in response to a correlated historical event, the set of liquidity rules generating a predicted balance for the account based on the identified patterns from the plurality of historical transaction data; and
transmitting, to a user … by the one or more [people], the set of liquidity rules;
… 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, such that application of the optimized transaction actions results in a higher balance of the client account, thereby improving the operation of the method for optimization.”
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 from the data, using a model (e.g. equation or stored instructions) to identify information and patterns, transmitting data for display, applying rules, and executing actions based on the 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 system to capture, analyze, transmit, apply, or execute 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 claims recite an abstract idea, mental process.
Additionally, the limitation of the claims 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 transaction data (e.g. financial information) and applying transaction actions based on rules with the goal 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 claims recite an abstract idea, certain methods of organizing human activity.
Additionally, the claims also recite Mathematical Concepts, wherein the method involves a “model”. The claim limitations to use a model to identify patterns (e.g. give an input to provide an output by using a model) may be interpreted as mathematical calculations (e.g. use an equation with an input to provide an output) under BRI. Therefore, the claims recite an abstract idea, mathematical concepts.
This judicial exception is not integrated into practical application. In particular, the claims recite an additional element of “computer” and “processor” to perform the method recited above by instructing the abstract idea to be performed “by” these generic computer components. As disclosed in Specification:
[00049] “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.”
the additional elements used for the claimed method may be any off-the-shelf generic computer system available to the public merely applied to perform its basic functionalities (e.g. capture data, extract data, transmit data, automatically apply or execute data, etc.), and does not require any specialized hardware or component to carry out the method. This generic computer 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. 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, transmit, apply, or execute data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) 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 “machine-learning model trained to identify patterns”. The machine learning model is recited at a basic level applied as a black-box model to provide inputs (e.g. plurality of item level features) to give outputs (e.g. identify patterns). There is no improvement upon the machine-learning model itself other than the mere recitation that the model had been trained, nor does applying the model have an impact, improvement, alterations, modifications, or any changes to the underlying technology (e.g. computer or processor), but the model is merely applied to implement the abstract idea of identifying patterns (i.e. mental process and/or mathematical concepts), wherein mere “apply it” is not indicative of integration into a practical application. Therefore, the claims are directed to an 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, 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 3 and 11 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 a predicted balance for the client account based on the identified patterns; and transmitting, to the user interface by the one or more processors, the predicted balance.” The claims recite additional steps of using the model to generate data and transmitting 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 5 and 13 recite “further comprising: 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; and transmitting, to the user interface by the one or more processors, the one or more client account reports.” The claims recite additional steps of using the model to identify data and transmitting 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 6 and 14 recite “wherein the natural language machine-learning model is an artificial intelligence model.” The claims provide additional details regarding the model, 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 7, 15, and 19 recite “wherein the plurality of historical transaction data comprises at least one of a funds transfer, a purchase, an account credit, or a payment.” The claims provide additional 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 8, 16, and 20 recite “wherein the plurality of item level features comprise numerical and/or textual data associated with the plurality of historical transaction data.” The claims provide additional 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 3, 5-8, 11, 13-16, and 19-20, 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, 3, 5-9, 11, 13-17, and 19-20 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, 9, 15, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kotarinos (US 20220122181 A1), in view of Brereton et al. (US 20150081542 A1), and in view of DUBEY et al. (US 20150339765 A1).
As per claims 1, 9, and 17, Kotarinos teaches a computer-implemented method for liquidity optimization, the method comprising:
capturing, by one or more processors, a plurality of … data of a client account ([0039] “To derive this set of preferences, a client 201 may provide responses on various questions 202 to extract relevant and material information about goals 210 to formulate a preference score”);
extracting, by the one or more processors, a plurality of item level features from the plurality of … data ([0039] “To derive this set of preferences, a client 201 may provide responses on various questions 202 to extract relevant and material information about goals 210 to formulate a preference score”);
providing, by the one or more processors, the plurality of item level features to a generative machine-learning model trained to identify patterns within the plurality of item level features and generate a set of liquidity rules for the client account based on the identified patterns, … the set of liquidity rules generating a projected balance for the client account based on the identified patterns from the plurality of historical transaction data ([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”);
transmitting, to a user interface by the one or more processors, the set of liquidity rules ([0057] “Since liquidity risk can be difficult to explain and visualize, bullet point descriptors are shown on the portfolio. The client can also see how liquidity issues would arise with an event-based explorer. In the event explorer, the portfolio's liquidity is shown across different scenarios that the client can interactively adjust … This process continues until the client chooses a portfolio from various options”);
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 set of liquidity rules, … thereby improving the operation of the method for optimization ([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”).
Although Kotarinos teaches of receiving data and extracting information from the data which is used as input to the for the machine learning process, the prior art does not seem to explicitly disclose that the data values are “plurality of historical transaction data”. However, Brereton teaches:
capturing, by one or more processors, a plurality of historical transaction 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 transaction 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”);
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize historical transaction data, as in Brereton in the system executing the method of Kotarinos, wherein Kotarinos already teaches of receiving data and extracting information to provide as inputs to the machine learning model, with the motivation of offering to [0021] “improve a level of confidence associated with identified patterns used to create the model parameters” as taught by Brereton over that of Kotarinos.
Although Kotarinos teaches of utilizing machine learning analysis to identify patterns and provide liquidity rules, the prior art does not seem to explicitly disclose that the patterns include a change in a balance of the client account in response to a correlated historical event. However, DUBEY teaches:
providing, by the one or more processors, the plurality of item level features to a generative machine-learning model trained to identify patterns within the plurality of item level features and generate a set of liquidity rules for the client account based on the identified patterns, the plurality of patterns including a change in a balance of the client account in response to a correlated historical event, the set of liquidity rules generating a predicted balance for the account based on the identified patterns from the plurality of historical transaction data ([0044] “In an embodiment, machine features 230 may be configured to process prior decisions and outcomes in the past, to support future user decision making and will be described in greater detail below. In particular, the machine learning 230 may find patterns within prior decisions and outcomes in the past for the purposes of making more accurate forecasting decisions which lead in better investment of the account balances (e.g. nostro account balances) for increased revenues and reduction in cost”);
…
executing automatically, by the one or more processors and on the client account, optimized transaction actions based on the applied set of liquidity rules, such that application of the optimized transaction actions results in a higher balance of the client account, thereby improving operation of the method for optimization ([0044] “In one embodiment, the machine learning 230 may provide suggested forecasting decisions in real time in situations where regular forecasting rules cannot make an automated decision or where the user requires additional assistance to make an informed decision. In another embodiment, the machine learning 230 may enhance the regular forecasting rules to further refine the automated forecasting decisions and improve the accuracy of the decisions as to whether a given forecast should be taken into position or not”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize data with prior decisions and outcomes in the past associated with account balances as in DUBEY in the system executing the method of Kotarinos, with the motivation of offering to [0044] lead in better investment of the account balances for increased revenues and reduction in cost, and improve accuracy of the machine learning model as taught by DUBEY over that of Kotarinos.
As per claims 7, 15, and 19, Kotarinos may not explicitly disclose, but Brereton teaches the computer-implemented method of claim 1, the system of claim 9, and the non-transitory computer-readable medium of claim 17, wherein the plurality of historical transaction 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”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize historical transaction data, as in Brereton in the system executing the method of Kotarinos, wherein Kotarinos already teaches of receiving data and extracting information to provide as inputs to the machine learning model, with the motivation of offering to [0021] “improve a level of confidence associated with identified patterns used to create the model parameters” as taught by Brereton over that of Kotarinos.
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Kotarinos, in view of Brereton, in view of DUBEY, and in view of MAITRA et al. (US 20200402156 A1).
As per claims 3 and 11, Kotarinos may not explicitly disclose, but MAITRA teaches the computer-implemented method of claim 2, and the system of claim 10, 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 Kotarinos, wherein Kotarinos 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 Kotarinos.
Claims 5-6 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Kotarinos, in view of Brereton, in view of DUBEY, and in view of Papadopoulos (US 20210109485 A1).
As per claims 5 and 13, Kotarinos may not explicitly disclose, but Papadopoulos teaches the computer-implemented method of claim 1, and the system of claim 9, further comprising:
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”); and
transmitting, to the user interface by the one or more processors, the one or more client account reports ([0146] “Any semantic data tag that is associated with a confidence score that does not exceed the confidence score threshold may be displayed in list of pending semantic data tags 908 … The user may view list of pending semantic data tags 908 and approve or disapprove of any semantic data tag suggestions. List of pending semantic data tags 908 may display any semantic data tags”).
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 Kotarinos, wherein Kotarinos 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 Kotarinos.
As per claims 6 and 14, Kotarinos teaches the computer-implemented method of claim 5, and the system of claim 13, wherein the natural language machine-learning model is an artificial intelligence model ([0043] “The assets 310 relationships over time 321 may be compared and analyzed in the algorithm 320. The preferred innovation uses time series analysis, machine learning 330, decision theory, and financial econometrics to more closely characterize the portfolio 311 based upon the utility matching metrics for the liquidity reference value. This is done by using time-series methods to analyze signal patterns across the portfolio 130, using machine learning to identify trends across large data environments, decision theory to analyze the artificial intelligence driven decision making process and financial econometrics to understand the financial implications of the decision”).
Claims 8, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kotarinos, in view of Brereton, in view of DUBEY, and in view of Malyack et al. (US 20190156253 A1).
As per claims 8, 16, and 20, Kotarinos may not explicitly disclose, but Malyack teaches the computer-implemented method of claim 1, the system of claim 9, and the non-transitory computer-readable medium of claim 17, wherein the plurality of item level features comprise numerical and/or textual data associated with the plurality of historical transaction 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 Kotarinos, 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 Kotarinos.
Response to Arguments
Applicant's arguments, see pages 9 to 11, filed 06-February-2026, 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, the claims recite an abstract idea related to data analysis (e.g. capture data, extract information, use a model to identify information or patterns, transmit data, apply rules, execute actions, etc.), wherein the data is associated with transaction data (e.g. financial information) and applying transaction actions with the goal to mitigate risk. The additional element of “machine learning model” is recited at a mere “apply it” level, and there is no improvement upon the machine-learning model itself other than the mere recitation that the model had been trained, nor does applying the model have an impact, improvement, alterations, modifications, or any changes to the underlying technology (e.g. computer or processor), but the model is merely applied to implement the abstract idea of identifying patterns (i.e. mental process and/or mathematical concepts), wherein mere “apply it” is not indicative of integration into a practical application. The claims, 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, and transmitting data. 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.
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)”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY H JUNG whose telephone number is (571)270-5018. The examiner can normally be reached Mon - Fri 9:30 - 5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine M Tran (Behncke) can be reached at (571) 272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HENRY H JUNG/ Examiner, Art Unit 3695
/CHRISTINE M Tran/ Supervisory Patent Examiner, Art Unit 3695
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