DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/3/23, 1/26/24, 5/15/24, 8/20/24, 9/19/24, 10/24/24, 12/4/24, 7/8/25, and 1/5/26 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are directed to a system or method, which are/is one of the statutory categories of invention. (Step 1: YES”).
The Examiner has identified independent method claim 17 as the claim that represents the claimed invention for analysis and is similar to independent system claims 1 and 12. Claim 17 recites the limitations of corelating user inputs to determine user’s assets/savings as compared to others.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Obtaining investment data of users; training and deploying ML model to process investment data and predict users investment percentage; determining an attribute level (note, “attribute level” may be savings, see para 117; comparing user’s savings to other users; and displaying the results, – specifically, the claim recites “training and deploying a machine learning model, the machine learning model being trained to process input data of a plurality of users to determine how the input data is related, the training including tuning parameters of the input data to correlate ascertained numerical levels to ascertained stored quantities; accessing user data of one or more user registers of a user to determine a user quantity stored in the one or more user registers; processing at least one user input associated with a numerical level; applying the deployed machine learning model to process at least the accessed user data and the at least one user input,
the applying generating an output comprising analysis of the user quantity and the at least one user input relative to ascertained numerical levels of multiple users of the plurality of users, the multiple users having an associated numerical level that is determined to be similar to the numerical level of the at least one user input; and displaying… the generated output comprising the analysis”, recites a fundamental economic practice, directed to mitigating risk.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice or commercial or legal interactions, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The “a computing system”, “a memory”, “one or more processors”, “program instructions”, “a machine learning model”, “a user interface”, and “a user device”, in claim 1, are just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claims 12 and 17 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
a computer such as a computing system, one or more processors, and a user device; a communication device such as a user interface; a storage unit such as a memory; and software module and algorithm such as program instructions and a machine learning model. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
The examiner notes that although the claim recites “a machine learning model”, it is recited at a high level. See claims 1, 12 and 17. For example, the claims simply state what the “a machine learning model” will do in the claimed business process – i.e. “to process input data of a plurality of users to determine how the input data is related”. See claim 1 and 17. Similarly the specification recites “a machine learning model” at a high level – see, for examples, paragraphs 111, 118, and 119. These are nominal recitations. The examiner notes that the applicant is not improving “a machine learning model”. Rather the applicant is using “a machine learning model”, in a business process. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, claims 1, 12, and 17 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 1, 12, and 17 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims further define the abstract idea that is present in their respective independent claims 1, 12, and 17 and thus correspond to Certain Methods of Organizing Human Activity, and hence are abstract for the reasons presented above.
Dependent claim 2 discloses the limitation of the ascertained numerical levels include remuneration levels of the plurality of users, and wherein the ascertained stored quantities include saved financial assets, which further narrows the abstract idea.
Dependent claim 3 discloses the limitation of the one or more user registers include one or more financial accounts, and wherein the user quantity includes one or more financial assets, which further narrows the abstract idea.
Dependent claim 4 discloses the limitation of the at least one user input that is received includes a remuneration amount of the user, which further narrows the abstract idea.
Dependent claim 5 discloses the limitation of the program instructions further receive the at least one user input from the user via the user device, which further narrows the abstract idea. Note that the technical element “the user device” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 discloses the limitation of the program instructions further ascertain the at least one user input from deposits made to the one or more user registers, the at least one user input including a culmination of the deposits over a designated period of time, which further narrows the abstract idea.
Dependent claim 7 discloses the limitation of the deposits include regular financial deposits determined to be associated with a remuneration received by the user, which further narrows the abstract idea.
Dependent claim 8 discloses the limitation of the analysis comprises a comparative analysis, and where the generated output displayed provides the user with a comparison of how the user quantity stored in the one or more user registers compares to ascertained stored quantities of the multiple users, which further narrows the abstract idea.
Dependent claim 9 discloses the limitation of based on the analysis determining that the user quantity stored in the one or more user registers is below an average of the ascertained stored quantities of the multiple users, the generated output includes a recommendation to increase the user quantity, which further narrows the abstract idea.
Dependent claim 10 discloses the limitation of the displaying is based on determining that the user is accessing, via the user device, a digital aggregation platform of a financial entity, which further narrows the abstract idea. Note that the technical element “the user device” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 11 discloses the limitation of the accessing, processing, applying and displaying is based on receiving a request, via the user device, from the user to determine how the user quantity stored in the one or more user registers compares to average stored quantities of individuals with remuneration levels similar to the user, which further narrows the abstract idea. Note that the technical element “the user device” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 13 discloses the limitation of the attribute level comprises a net worth of the user, which further narrows the abstract idea.
Dependent claim 14 discloses the limitation of the attribute level comprises a yearly remuneration level of the user, which further narrows the abstract idea.
Dependent claim 15 discloses the limitation of the determining, applying and displaying is based on receiving a request, via the user device, from the user for the generated output, which further narrows the abstract idea. Note that the technical element “the user device” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 16 discloses the limitation of the investment data of the multiple users includes investment percentages of assets, and wherein the results of the comparative analysis compare the one or more investments of the user to the investment percentages of assets, which further narrows the abstract idea.
Dependent claim 18 discloses the limitation of the ascertained numerical levels include remuneration levels of the plurality of users, and wherein the ascertained stored quantities include saved financial assets, which further narrows the abstract idea.
Dependent claim 19 discloses the limitation of the one or more user registers include one or more financial accounts, and wherein the user quantity includes one or more financial assets, which further narrows the abstract idea.
Dependent claim 20 discloses the limitation of the at least one user input that is received includes a remuneration amount of the user, which further narrows the abstract idea.
Thus, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Claim Rejections - 35 USC § 103
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 of this title, 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, 3, 5-6, 8-13, and 15-20 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Dolan (20220230236) in view of Shin (KR20100003154A).
Regarding claim 1, Dolan discloses
a computing system for machine learning, the system comprising: a memory; one or more processors in communication with the memory; and program instructions executable by the one or more processors via the memory to
(“[0004] A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. One general aspect includes an artificial intelligence (AI) architecture involving a support vector machine (SVM) engine. The AI architecture may comprise a transaction database that may include”).
train and deploy a machine learning model
(“[0004] One general aspect includes an artificial intelligence (AI) architecture involving a support vector machine (SVM) engine. The AI architecture may comprise a transaction database that may include… The AI architecture may include an AI model generated by a training engine trained with the dictionary data store and the account transfer information”).
the training including tuning parameters of the input data to correlate ascertained numerical levels to ascertained stored quantities;
(The examiner notes that “tuning parameters” can be very broad, as the specification states “grid search, random search, Bayesian optimization, or various other techniques used to maximize the machine model's performance (emphasis examiner’s)”. See paragraph 106. As such, the relevant prior art teaching are – “[0005] Implementations may include one or more of the following features. The training engine may include a support vector machine (SVM). The training engine may include a pattern recognition engine”).
access user data of one or more user registers of a user to determine a user quantity stored in the one or more user registers;
(The examiner notes that “user quantity” can be savings. See specification paragraph 109. Similarly, “stored quantities” can also be savings. See specification paragraph 107. As such, the relevant prior art teaching are – (“[0037] In one embodiment, computer implemented systems and methods are disclosed for processing a financial transaction that may include determining a transfer amount, determining accounts associated with transfers, determining trigger events and rules associated with transfers, etc.… The system includes computing systems that operate to process transactions associated with various types of accounts. Examples of such accounts may include a checking account, a savings account, a merchant account, and investment account”).
process at least one user input associated with a numerical level; apply the deployed machine learning model to process at least the accessed user data and the at least one user input
(“[0090] Additionally, or alternatively, the ML engine 150-5 may use a look-up table to determine an account transfer. The look-up table may comprise memo words/phrases, corresponding savings goals, and a value of the transfer. Based on the look-up table and the words/phrases included in the electronic fund transfer request (e.g., sent at step 404), the ML engine 150-5 may determine a corresponding savings goal and a value of the transfer. The ML engine 150-5 may determine (at step 412) an account transfer from the checking account to a savings account associated with the determined savings goal.
[0091] The ML engine 150-5 may additionally determine a value of the account transfer. The value of the account transfer may be based on values of historical account transfers as initiated by the client (e.g., at step 320 in FIG. 3). For example, the value of the account transfer between two accounts may be equal to a value of a previous transfer between the two accounts. Alternatively, the value of the account transfer may be configured by the client. The value of the account transfer may be a specific dollar value (e.g., $2, $5, etc.) or a percentage of the value of the electronic fund transfer”).
the applying generating an output comprising analysis of the user quantity and the at least one user input relative to ascertained numerical levels of multiple users of the plurality of users, the multiple users having an associated numerical level that is determined to be similar to the numerical level of the at least one user input; and
(“[0080] As another example, the ML engine 150-5 may determine a spending pattern of a client based on MCCs, merchant IDs, etc. for a client as stored in a database. The ML engine 150-5 may compare a savings rate (e.g., amount transferred to a savings account per month) for a client with savings rates of other clients with the similar spending pattern. Spending pattern may correspond to an amount spent for different categories of merchants (e.g., groceries, travel, clothing, etc.). If the savings rates for the client is lower than an average savings rates for the other clients, the ML engine 150-5 may prescribe/indicate, to the client, a recommendation to increase their savings rate”).
display, via a user interface of a user device, the generated output comprising results of the analysis
(“[0093] FIG. 5 illustrates an example GUI 500 that may be displayed at the user computing device 120 based on receiving an account transfer notification, in accordance with one or more example arrangements. The GUI 500 may display a transfer amount 505, a source account 510 and a destination account 515. For example, if the ML engine 150-5 determines an account transfer from the checking account to the “homeownership” account, the GUI 500 may indicate the checking account and the “homeownership” account. The client may modify details associated with the account transfer (e.g., source account, destination account, and/or transfer value, etc.) using the GUI 500”).
Dolan does not disclose, however, Shin teaches
[the machine learning model being trained to process input data of a plurality of users to determine how the input data is related]
(Page 4 paragraph 4, “Financial forecasting system to predict how the customer's financial status, receiving an input from a customer assets / debt information and income / expenses / other; The property / real estate information contained in the debt and other fixed assets, asset information based on the information, time Step of identifying the value of the full change with the lapse of fixed assets; The assets / liabilities and income on the basis of information / expenses / other information, the method comprising: identifying a change in the value of available liquid assets liquidated over time; The asset / liability information group of real estate information contained in the second, the method comprising: identifying a value of a residential property change with the lapse of time; The fixed asset value analysis unit and the flow on the basis of the change in the value of property changes and capital assets identified in the analysis unit, the method comprising: identifying a change in the value of net assets customer with the lapse of time; And a step of displaying the identified fixed assets, current assets, value change with the passage of time and the net assets of residential property”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Dolan to include [the machine learning model being trained to process input data of a plurality of users to determine how the input data is related] as taught by Y3 to be able to collect related financial information to forecast user’s future financial condition. See page 5 paragraph 6, the net asset with the lapse of time and fixed assets by analyzing the relationship between the (in particular, a residential property), it is possible to provide a method and system for financial forecasts predict future financial conditions. In addition, the economic agent can provide a balance prediction method and system for predicting the change with time of death balance.
Regarding claim 3, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
Dolan further discloses
the one or more user registers include one or more financial accounts, and wherein the user quantity includes one or more financial assets
(“[0037] In one embodiment, computer implemented systems and methods are disclosed for processing a financial transaction that may include determining a transfer amount, determining accounts associated with transfers, determining trigger events and rules associated with transfers, etc. … The system includes computing systems that operate to process transactions associated with various types of accounts. Examples of such accounts may include a checking account, a savings account, a merchant account, and investment account”).
Regarding claim 5, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
Dolan further discloses
the program instructions further receive the at least one user input from the user via the user device
(“[0037] In one embodiment, computer implemented systems and methods are disclosed for processing a financial transaction that may include determining a transfer amount, determining accounts associated with transfers, determining trigger events and rules associated with transfers, etc. … The system includes computing systems that operate to process transactions associated with various types of accounts. Examples of such accounts may include a checking account, a savings account, a merchant account, and investment account, and one or more computer systems and mobile devices including a communication interface”).
Regarding claim 6, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
Dolan further discloses
the program instructions further ascertain the at least one user input from deposits made to the one or more user registers, the at least one user input including a culmination of the deposits over a designated period of time
(“[0037] In one embodiment, computer implemented systems and methods are disclosed for processing a financial transaction that may include determining a transfer amount, determining accounts associated with transfers, determining trigger events and rules associated with transfers, etc. … The system includes computing systems that operate to process transactions associated with various types of accounts. Examples of such accounts may include a checking account, a savings account, a merchant account, and investment account”).
Regarding claim 8, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
Dolan further discloses
the analysis comprises a comparative analysis, and where the generated output displayed provides the user with a comparison of how the user quantity stored in the one or more user registers compares to ascertained stored quantities of the multiple users
[0080] As another example, the ML engine 150-5 may determine a spending pattern of a client based on MCCs, merchant IDs, etc. for a client as stored in a database. The ML engine 150-5 may compare a savings rate (e.g., amount transferred to a savings account per month) for a client with savings rates of other clients with the similar spending pattern. Spending pattern may correspond to an amount spent for different categories of merchants (e.g., groceries, travel, clothing, etc.). If the savings rates for the client is lower than an average savings rates for the other clients, the ML engine 150-5 may prescribe/indicate, to the client, a recommendation to increase their savings rate”).
Regarding claim 9, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
Dolan further discloses
based on the analysis determining that the user quantity stored in the one or more user registers is below an average of the ascertained stored quantities of the multiple users, the generated output includes a recommendation to increase the user quantity
[0080] As another example, the ML engine 150-5 may determine a spending pattern of a client based on MCCs, merchant IDs, etc. for a client as stored in a database. The ML engine 150-5 may compare a savings rate (e.g., amount transferred to a savings account per month) for a client with savings rates of other clients with the similar spending pattern. Spending pattern may correspond to an amount spent for different categories of merchants (e.g., groceries, travel, clothing, etc.). If the savings rates for the client is lower than an average savings rates for the other clients, the ML engine 150-5 may prescribe/indicate, to the client, a recommendation to increase their savings rate”).
Regarding claim 10, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
Dolan further discloses
the displaying is based on determining that the user is accessing, via the user device, a digital aggregation platform of a financial entity
(“[0093] FIG. 5 illustrates an example GUI 500 that may be displayed at the user computing device 120 based on receiving an account transfer notification, in accordance with one or more example arrangements. The GUI 500 may display a transfer amount 505, a source account 510 and a destination account 515. For example, if the ML engine 150-5 determines an account transfer from the checking account to the “homeownership” account, the GUI 500 may indicate the checking account and the “homeownership” account. The client may modify details associated with the account transfer (e.g., source account, destination account, and/or transfer value, etc.) using the GUI 500”).
Regarding claim 11, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
Dolan further discloses
the accessing, processing, applying and displaying is based on receiving a request, via the user device, from the user to determine how the user quantity stored in the one or more user registers compares to average stored quantities of individuals with remuneration levels similar to the user
(“[0093] FIG. 5 illustrates an example GUI 500 that may be displayed at the user computing device 120 based on receiving an account transfer notification, in accordance with one or more example arrangements. The GUI 500 may display a transfer amount 505, a source account 510 and a destination account 515. For example, if the ML engine 150-5 determines an account transfer from the checking account to the “homeownership” account, the GUI 500 may indicate the checking account and the “homeownership” account. The client may modify details associated with the account transfer (e.g., source account, destination account, and/or transfer value, etc.) using the GUI 500”).
Claim 12 is rejected using the same rationale that was used for the rejection of claim 1.
Regarding claim 13, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
Dolan further discloses
the attribute level comprises a net worth of the user
(“[0090] Additionally, or alternatively, the ML engine 150-5 may use a look-up table to determine an account transfer. The look-up table may comprise memo words/phrases, corresponding savings goals, and a value of the transfer. Based on the look-up table and the words/phrases included in the electronic fund transfer request (e.g., sent at step 404), the ML engine 150-5 may determine a corresponding savings goal and a value of the transfer. The ML engine 150-5 may determine (at step 412) an account transfer from the checking account to a savings account associated with the determined savings goal.
[0091] The ML engine 150-5 may additionally determine a value of the account transfer. The value of the account transfer may be based on values of historical account transfers as initiated by the client (e.g., at step 320 in FIG. 3). For example, the value of the account transfer between two accounts may be equal to a value of a previous transfer between the two accounts. Alternatively, the value of the account transfer may be configured by the client. The value of the account transfer may be a specific dollar value (e.g., $2, $5, etc.) or a percentage of the value of the electronic fund transfer”).
Claim 15 is rejected using the same rationale that was used for the rejection of claim 5.
Regarding claim 16, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
Dolan further discloses
the investment data of the multiple users includes investment percentages of assets, and wherein the results of the comparative analysis compare the one or more investments of the user to the investment percentages of assets
[0080] As another example, the ML engine 150-5 may determine a spending pattern of a client based on MCCs, merchant IDs, etc. for a client as stored in a database. The ML engine 150-5 may compare a savings rate (e.g., amount transferred to a savings account per month) for a client with savings rates of other clients with the similar spending pattern. Spending pattern may correspond to an amount spent for different categories of merchants (e.g., groceries, travel, clothing, etc.). If the savings rates for the client is lower than an average savings rates for the other clients, the ML engine 150-5 may prescribe/indicate, to the client, a recommendation to increase their savings rate”).
Claim 17 is rejected using the same rationale that was used for the rejection of claim 1.
Claim 18 is rejected using the same rationale that was used for the rejection of claim 2.
Claim 19 is rejected using the same rationale that was used for the rejection of claim 3.
Claim 20 is rejected using the same rationale that was used for the rejection of claim 4.
Claims 2, 4, 7, and 14 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Dolan in view of Shin, further in view of and Ezov (20210042629).
Regarding claim 2, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
Dolan further discloses
the ascertained numerical levels include [remuneration levels] of the plurality of users, and wherein the ascertained stored quantities include saved financial assets
(“[0037] In one embodiment, computer implemented systems and methods are disclosed for processing a financial transaction that may include determining a transfer amount, determining accounts associated with transfers, determining trigger events and rules associated with transfers, etc. … The system includes computing systems that operate to process transactions associated with various types of accounts. Examples of such accounts may include a checking account, a savings account, a merchant account, and investment account ”).
Dolan does not disclose
remuneration levels.
However, Ezov teaches
remuneration levels
(“0027] Given a data instance, a subset of the features, of the alternative features, or the like, may suffice for predicting a label. Referring again to the above age and salary example, the bank may decide that given a salary below 50,001$, a loan will never be granted, regardless of the age. Hence, if the user inputs her salary matching the lowest range [0-50,000], there may be no need to receive additional information in order to make a prediction of the label. In that case, the predictive model may predict, based on that feature alone, that the loan will be declined. As a result, there is no need to obtain from the user a value for the age feature. In such a case, the user divulges even less private information, without adversely affecting the quality of the prediction”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combination Dolan and Shin to include remuneration levels as taught by Ezov to use the customer’s salary input to predict financial goal/sufficiency without having to gather a very comprehensive data set. See “0027] Additionally or alternatively, the UI elements may correspond to a portion of the alternative features, to a subset of the set of alternative features, or the like. Given a data instance, a subset of the features, of the alternative features, or the like, may suffice for predicting a label. Referring again to the above age and salary example, the bank may decide that given a salary below 50,001$, a loan will never be granted, regardless of the age. Hence, if the user inputs her salary matching the lowest range [0-50,000], there may be no need to receive additional information in order to make a prediction of the label. In that case, the predictive model may predict, based on that feature alone, that the loan will be declined. As a result, there is no need to obtain from the user a value for the age feature. In such a case, the user divulges even less private information, without adversely affecting the quality of the prediction.”
Regarding claim 4, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
The combination of Dolan and Shin do not disclose but Ezov does teaches
the at least one user input that is received includes a remuneration amount of the user
(“0027] Given a data instance, a subset of the features, of the alternative features, or the like, may suffice for predicting a label. Referring again to the above age and salary example, the bank may decide that given a salary below 50,001$, a loan will never be granted, regardless of the age. Hence, if the user inputs her salary matching the lowest range [0-50,000], there may be no need to receive additional information in order to make a prediction of the label. In that case, the predictive model may predict, based on that feature alone, that the loan will be declined. As a result, there is no need to obtain from the user a value for the age feature. In such a case, the user divulges even less private information, without adversely affecting the quality of the prediction”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combination Dolan and Shin to include the at least one user input that is received includes a remuneration amount of the user as taught by Ezov to use the customer’s salary input to predict financial goal/sufficiency without having to gather a very comprehensive data set. See “0027] Additionally or alternatively, the UI elements may correspond to a portion of the alternative features, to a subset of the set of alternative features, or the like. Given a data instance, a subset of the features, of the alternative features, or the like, may suffice for predicting a label. Referring again to the above age and salary example, the bank may decide that given a salary below 50,001$, a loan will never be granted, regardless of the age. Hence, if the user inputs her salary matching the lowest range [0-50,000], there may be no need to receive additional information in order to make a prediction of the label. In that case, the predictive model may predict, based on that feature alone, that the loan will be declined. As a result, there is no need to obtain from the user a value for the age feature. In such a case, the user divulges even less private information, without adversely affecting the quality of the prediction.”
Regarding claim 7, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
The combination of Dolan and Shin do not disclose but Ezov does teaches
the deposits include regular financial deposits determined to be associated with a remuneration received by the user
(“0027] Given a data instance, a subset of the features, of the alternative features, or the like, may suffice for predicting a label. Referring again to the above age and salary example, the bank may decide that given a salary below 50,001$, a loan will never be granted, regardless of the age. Hence, if the user inputs her salary matching the lowest range [0-50,000], there may be no need to receive additional information in order to make a prediction of the label. In that case, the predictive model may predict, based on that feature alone, that the loan will be declined. As a result, there is no need to obtain from the user a value for the age feature. In such a case, the user divulges even less private information, without adversely affecting the quality of the prediction”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combination Dolan and Shin to include the deposits include regular financial deposits determined to be associated with a remuneration received by the user as taught by Ezov to use the customer’s salary input to predict financial goal/sufficiency without having to gather a very comprehensive data set. See “0027] Additionally or alternatively, the UI elements may correspond to a portion of the alternative features, to a subset of the set of alternative features, or the like. Given a data instance, a subset of the features, of the alternative features, or the like, may suffice for predicting a label. Referring again to the above age and salary example, the bank may decide that given a salary below 50,001$, a loan will never be granted, regardless of the age. Hence, if the user inputs her salary matching the lowest range [0-50,000], there may be no need to receive additional information in order to make a prediction of the label. In that case, the predictive model may predict, based on that feature alone, that the loan will be declined. As a result, there is no need to obtain from the user a value for the age feature. In such a case, the user divulges even less private information, without adversely affecting the quality of the prediction.”
Regarding claim 14, the combination of Dolan and Shin, as shown in the rejection above, discloses the limitations of claim 1.
The combination of Dolan and Shin do not disclose but Ezov does teaches
the attribute level comprises a yearly remuneration level of the user
(“0027] Given a data instance, a subset of the features, of the alternative features, or the like, may suffice for predicting a label. Referring again to the above age and salary example, the bank may decide that given a salary below 50,001$, a loan will never be granted, regardless of the age. Hence, if the user inputs her salary matching the lowest range [0-50,000], there may be no need to receive additional information in order to make a prediction of the label. In that case, the predictive model may predict, based on that feature alone, that the loan will be declined. As a result, there is no need to obtain from the user a value for the age feature. In such a case, the user divulges even less private information, without adversely affecting the quality of the prediction”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combination Dolan and Shin to include the attribute level comprises a yearly remuneration level of the user as taught by Ezov to use the customer’s salary input to predict financial goal/sufficiency without having to gather a very comprehensive data set. See “0027] Additionally or alternatively, the UI elements may correspond to a portion of the alternative features, to a subset of the set of alternative features, or the like. Given a data instance, a subset of the features, of the alternative features, or the like, may suffice for predicting a label. Referring again to the above age and salary example, the bank may decide that given a salary below 50,001$, a loan will never be granted, regardless of the age. Hence, if the user inputs her salary matching the lowest range [0-50,000], there may be no need to receive additional information in order to make a prediction of the label. In that case, the predictive model may predict, based on that feature alone, that the loan will be declined. As a result, there is no need to obtain from the user a value for the age feature. In such a case, the user divulges even less private information, without adversely affecting the quality of the prediction.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Macdonald (20160117771) teaches centralized and customized asset allocation recommendation and planning using personalized profiling.
Pinkas (20080162377) teaches system and method of managing cash and suggesting transactions in a multi-strategy portfolio.
Mora (20240078605) teaches a dynamic computing system for asset management.
Sion (20160034932) teaches methods and apparatus for promoting financial behavioral change.
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/MARK H GAW/Examiner, Art Unit 3693