Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/758,304, filed on June 28, 2024. In response to Examiner’s Non-Final Office Action of October 30, 2025, Applicant, on January 30, 2026, amended claims 1, 9 and 17; and cancelled claims 8, 16 and 20. Claims 1-7, 9-15, and 17-19 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding 35 U.S.C. § 101 rejection, the amendment has been considered and is insufficient to overcome the rejection.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to applicants
amendments. Updated 35 U.S.C. § 103 rejections have been applied to amended
claims. Please refer to the § 103 rejection for further explanation and rationale.
The Double Patenting rejection has been withdrawn.
Response to Arguments
Applicant’s arguments filed January 30, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed January 30, 2026.
On page 9-10 of the Remarks regarding 35 U.S.C. § 101, Applicant states the claims do not recite mental processes because Claim l's limitations cannot be practically performed in the human mind. In response, regarding the 35 U.S.C. § 101 rejection, Examiner finds under the broadest reasonable interpretation capturing a plurality of historical transaction data of a client account; and extracting, a plurality of item level features from the plurality of historical transaction data, falls within the Abstract idea grouping of “Mental Processes” – evaluation. he claims primarily recite the additional element of using computer components to perform each step. The “processor”, “system”; “memory”; “user interface”, and “computer readable medium”, is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). The general use of a machine learning analysis (including encoded textual descriptors) does not provide a meaningful limitation to transform the abstract idea into a practical application.
On Pg. 11-13 regarding the 35 U.S.C. § 103 rejection, Applicant argues prior art fails to disclose amended claim language. In response, new ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Malyack is now applied for Claims 1 and 9 and 17. Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
Priority
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.
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 1-7, 9-15, and 17-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-7, 9-15, and 17-19 are directed to rules based modelling.
Claim 1 recites a method for rules based modelling, Claim 9 recites a system for rules based modelling and Claim 17 recites an article of manufacture for rules based modelling, which include capturing a plurality of historical transaction data of a client account; extracting a plurality of item level features from the plurality of historical transaction data, the plurality of item level features including normalized numerical attributes and encoded textual descriptors associated with individual historical transactions ; providing the plurality of item level features to a predictive machine-learning model trained to identify patterns within the plurality of item level features and generate a projected balance for the client account based on the identified patterns; and transmitting the projected balance to a user interface.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “processor”, “system”; “memory”; “user interface”, and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Mental Processes”-evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “processor”, “system”; “memory”; “user interface”, and “computer readable medium”, is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1, claim 9 and claim 17 recite using one or more machine learning analysis techniques. The specification discloses the machine learning analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the natural language processing is solely used a tool to perform the instructions of the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in modelling.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “processor”, “system”; “memory”; “user interface”, and “computer readable medium”, is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-7, 10-15 and 18-19 recite providing 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; and transmitting, to the user interface by the set of liquidity rules; applying the set of liquidity rules to the client account; and executing on the client account, optimized transaction actions based on the set of liquidity rules; providing the optimized transaction actions to the 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 the predicted balance; providing 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 the one or more client account reports; wherein the natural language machine-learning model is an artificial intelligence model; wherein the plurality of historical transaction data comprises at least one of a funds transfer, a purchase, an account credit, or a payment; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 9 and 17. Regarding Claims, 2-5, 10-13 and 18 and the additional elements of “processor” and “user interface” - it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claims 2, 4-6, 10, 12-14 and claim 18 and the additional element of machine learning - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
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.
Claims 1-4, 7, 9-12, 15 and 17-19 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), in further view of Malyack et al. (US 20190156253 A1) and in further view of MAITRA et al. (US 20200402156 A1).
As per claims 1, 9, and 17, Kotarinos teaches a computer-implemented method for rules based modelling, 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 predictive machine-learning model trained to identify patterns within the plurality of item level features … ([0057-0058] “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 … 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”; Par. 17-predictive; Par. 49-); and
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.
Kotarinos in view of Brereton may not explicitly disclose, but Malyack teaches the plurality of item level features including normalized numerical attributes and encoded textual descriptors associated with individual historical transactions ([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)”); [0120] “In some examples, the training engine 702 comprises a normalization module 706 and a feature extraction module 704. The normalization module 706, in some examples, may be configured to normalize (e.g., via Z-score methods) the historical data so as to enable different data sets to be compared. Normalization is the process of changing one or more values in a data set (e.g., the volume forecast data management tool 715) to a common scale while maintaining the general distribution and ratios in the data set. In this way, although values are changed, differences between actual values in the data set are not distorted such that information is not lost. For example, values from the volume forecast data management tool 715 may range from 0 to 100,000. The extreme difference in this scale may cause problems when combining these values into the same features for modeling. In an example illustration, this range can be changed to a scale of 0-1 or represent the values as percentile ranks, as opposed to absolute values.”).
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 in view 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 Kotarinos in view of Brereton.
Kotarinos in view of Brereton in further view of Malyack may not explicitly disclose, but MAITRA teaches:
providing, by the one or more processors, the plurality of item level features to a generative machine-learning model trained using historical client-account transaction datasets 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 ([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”; [0013] Additionally, the forecasting model may rely on historical data relating to transactions of the account, as well as textual descriptions associated with the transactions to make a prediction; 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
and transmitting the projected balance to a user interface by the one or more processors ([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 in view Brereton in further view of Malyack, 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 in view Brereton in further view of Malyack.
As per claims 2, 10, and 18, Kotarinos teaches the computer-implemented method of claim 1, the system of claim 9, and the non-transitory computer-readable medium of claim 17, further comprising:
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 ([0057-0058] “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 … 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”; Par. 17-predictive; Par. 49; [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
and transmitting, to the 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”).
As per claims 3, and 11, Kotarinos teaches the computer-implemented method of claim 2, the system of claim 10, …
applying, 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, by the one or more processors and on the client account, optimized transaction actions based on the 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”).
As per claims 4 and 12, Kotarinos in view Brereton in further view of Malyack 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 a 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 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 in view Brereton in further view of Malyack, 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 in view Brereton in further view of Malyack.
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 5-6 and 13-14 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), in further view of Malyack et al. (US 20190156253 A1)in further view of MAITRA et al. (US 20200402156 A1), and in further 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 in view of Brereton in further view of Malyack in further view of Maitra, 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 in view of Brereton in further view of Malyack in further view of Maitra.
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 - Cancelled
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Muse (US 20220059230 A1) ([0189] “As previously noted, the adjustment generation machine learning model 355 in some embodiments may comprise a time series prediction model and an adjustment application model. The time series prediction model is configured to determine a projected balance of the end user's financial instrument over a prospective period of time, and the adjustment application model is configured to generate the adjustment model 360 based on the projected balance and the risk model 340”) 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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624
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