DETAILED ACTION
This rejection is in response to Request for Continued Examination filed 06/10/2026.
Claims 2, 4-5, 8-14 and 16-19 are currently pending and have been examined.
Claims 1, 3, 6-7, 15, and 20 are cancelled.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/10/2026 has been entered.
Response to Arguments
Applicant’s arguments, see pages 1-2, filed 05/11/2026, with respect to 35 U.S.C. 112(b) rejection and non-statutory double patenting rejection have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection and non-statutory double patenting rejection have been withdrawn.
Applicant's arguments filed 05/11/2026 have been fully considered but they are not persuasive.
With respect to applicant’s arguments on page 2-5 of remarks filed 05/11/2026 the claims are not directed to an abstract idea because training the machine learning model cannot be practically be performed by the human mind, similar to Example 39, Examiner respectfully disagrees.
One of the enumerated groupings of abstract ideas is defined as certain methods of organizing human activity that includes fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2).
The claims are directed to certain methods of organizing human activity as it relates to sales activities and commercial interactions because the claim includes determining the financial wellness of a person by collecting and processing data, predicting factors for determining financial wellness. The claims are not being analyzed as directed towards a mental process. The training of the machine learning model is not considered as directed towards certain methods of organizing human activity.
The instant claims recite functions of a machine learning model (e.g. training, predicting, indicate modifications, and deploy). Example 39 was not considered as patent eligible for merely reciting basic functions of a neural network that involve training and using the model, but it involved more than just using a neural network such as image transformations and creating multiple training sets with modified images. The instant claims are not analogous to Example 39 because instant claims do not involve any steps related to image transformations or creating new modified images.
With respect to applicant’s arguments on page 6-10 of remarks filed 05/11/2026 the claims are directed to a practical application because the recited additional elements as a whole reflects an improvement in the functioning of technology by improving the nodes of a machine learning model by repeatedly predicting a value during a plurality of iterations and applying differing weights to nodes that send, receive, and forwarding information, similar to Example 47, Examiner respectfully disagrees.
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. See MPEP § 2106.05(a).
To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP § 2106.05(f) and MPEP § 2106.05(a)(II).
It is unclear to one of ordinary skill in the art how the node of the machine learning model is improved by applying differing weights to the nodes of the machine learning model. The improvement to the prediction factor for determining a financial wellness of a person may solve a commercial problem to provide more accurate predictions, however, this does not solve a problem rooted in technology or the node of the machine learning model. Applicant’s specification describes applying weights to the nodes to obtain a probability and that a neural network generally includes nodes but does not provide further detail as to how the node of the machine learning model is improved such as in paragraphs:
[0063]: …[a] neural network generally includes connected units, neurons, or nodes (e.g., connected by synapses)…;
[0064]: …[d]eep learning typically employs a software structure comprising several layers of neural networks that perform nonlinear processing, where each successive layer receives an output from the previous layer. Generally, the layers include an input layer that receives raw data from a sensor, a number of hidden layers that extract abstract features from the data, and an output layer that identifies a certain thing based on the feature extraction from the hidden layers. The neural networks include neurons or nodes that each has a “weight” that is multiplied by the input to the node to obtain a probability of whether something is correct. More specifically, each of the nodes has a weight that is a floating point number that is multiplied with the input to the node to generate an output for that node that is some proportion of the input. The weights are initially “trained” or set by causing the neural networks to analyze a set of known data under supervised processing and through minimizing a cost function to allow the network to obtain the highest probability of a correct output.
[0070] A weight defines the impact a node in any given layer has on computations by a connected node in the next layer. FIG. 5 shows a network 150 including a node 152 in a hidden layer. The node 152 is connected to several nodes in the previous layer representing inputs to the node 152. Input nodes 154, 156, 158 and 160 in an input layer 162 are each assigned a respective weight W01, W02, W03, and W04 in the computation at the node 152, which in this example is a weighted sum.
[0084]: Thus, the processor 254 may include, among other devices and components, one or more neural networks 256 having trained and weighted nodes 258. The nodes 258 in the neural network 256 would be weighted and trained for determining and monitoring the financial and physical wellbeing of the person 252 as discussed herein
Therefore, the additional elements (e.g. server, memory device, processor, machine learning model) do not integrate the judicial exception into a practical application.
The instant claims are not analogous to claim 3 of Example 47 because the instant claims do not provide an improvement to the network like Example 47. The instant claims improving data analysis to provide a more comprehensive process that determines a financial wellness of a person, may provide more accurate predictions but it does not provide an improvement technology. Merely adding generic computer components to perform the method is not sufficient. Therefore, the instant claims must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology.
With respect to applicant’s arguments on page 10-11 of remarks filed 05/11/2026 that the combination of limitations are not well-understood, routine, or generic because the claims use the field of statistical analysis to determine likelihood of user attrition, Examiner respectfully disagrees.
The claims are not being analyzed as well-understood, routine, or generic. However, similar to the analysis under Step 2A (prong 2), the claims do not recite significantly more than the judicial exception because the claims do not include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. Therefore, the claims do not recite significantly more than the judicial exception.
With respect to applicant’s arguments on page 11 of remarks filed 05/11/2026 that Chintakindi is not used to determine economic or financial health or wellbeing of a person but rather look at purchases to help determine the person’s physical wellbeing, Examiner respectfully disagrees.
Chintakindi teaches that the financial wellness is a combination of a financial wellbeing and a physical wellbeing because this reference teaches that a user's health and wellness is determined based on an aggregation of a plurality of data including both physical and health data in combination with financial transaction data where the machine learning evaluates the user’s health which is also associated with savings (e.g. lower premium, discount, or the like). For example a combination of the health and financial data is associated with savings for life insurance premiums. (Chintakindi, [0043]; [0072]; [0128]; [0429]; [0430]; [0431]; [0110]; [0396]).
With respect to applicant’s arguments on page 11-12 of remarks filed 05/11/2026 that Sanghvi does not teach all of the collected data and Douthit does not training the neural network to determine financial and physical wellbeing of the person, Examiner respectfully disagrees.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 4-5, 8-14 and 16-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2 and 16 recites: inserting the training data into an iterative training and testing loop to predict a target variable; and repeatedly predicting a value for the target variable during a plurality of iterations of the training and testing loop, each iteration of the plurality of iterations having differing weights applied to one or more nodes of the machine learning model, each of the differing weights being updated with each respective iteration of the training and testing loop, rendering said claims indefinite because it is unclear whether the first recitation of testing loop is the same or different from the subsequent recitations of testing loop. Appropriate correction or clarification is required.
There is insufficient antecedent basis in the following limitations in:
Claims 2 and 16 recite:
the training data;
the processed data.
Claims 5 and 17 recite:
the interactions…the banks clients.
Appropriate correction or clarification is required.
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 2, 4-5, 8-14 and 16-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test, it must be considered whether the claims are directed to one of the four statutory classes of invention. See MPEP § 2106. In the instant case, claims 2, 4-5, 8-14 are directed to a system, and claims 16-19 are directed to a method (which falls within one of the four statutory categories of invention (process/apparatus). Accordingly, the claims will be further analyzed under revised step 2:
Under step 2A (prong 1) of the Subject Matter Eligibility Test, it must be considered whether the claims recite a judicial exception if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. If the claim recites a judicial exception (i.e., an abstract idea), the claim requires further analysis in Prong Two. One of the enumerated groupings of abstract ideas is defined as certain methods of organizing human activity that includes fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2).
Regarding representative independent claim 2, recites the abstract idea of:
…wherein the financial wellness is a combination of a financial wellbeing and a physical wellbeing of the person, wherein a bank is determining the financial wellness of the person and the person is a client of the bank,…;
…to predict factors for determining a financial wellness of the person, the training including:
collect data as it is being received over time about the financial wellness of the person… collects the data about the financial wellness of the person from a financial monitoring source that provides significant credit score changes, changes in direct deposit patterns or income changes including loss of employment and reduction in work hours, changes in transaction and account patterns including account closures, frequent overdrafts, late payments and sudden increase in debt-related transactions, changes in credit card patterns including increased transaction frequency for basic needs with decreased spending in dining out and entertainment, sale of investments or assets, requests for payment extensions or loan modifications and payday loans or cash advances and from a money and a mindset source that provides a determination of a financial literacy of the person; perform data cleaning on the ingested data;
…predict factors for determining the financial wellness of the person,…
process the collected data as it is being received over time…;
determine the financial wellness of the person based on the processed data and information.
The above-recited limitations amounts to certain methods of organizing human activity as it relates to sales activities and commercial interactions because the claim recites determining the financial wellness of a person by collecting data, processing the collected data, and predicting factors for determining financial wellness.
Accordingly, the claim recites an abstract idea. See MPEP § 2106.
The Step 2A (prong 2) of the Subject Matter Eligibility Test, is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.
In this instance, the claims recite the additional elements such as:
A system for determining a financial wellness of a person over time, said system comprising: a back-end server including: at least one processor; a communications interface communicatively coupled to the at least one processor; and a memory device storing executable code that, when executed, causes the at least one processor to: (Claim 2);
…,said collected data being stored as ingested data, wherein the at least one processor…; transform the ingested data from a plurality of data formats into a standardized training format for training a machine learning model; train, using training test data of the ingested data the machine learning model to …; the training including: inserting the training data into an iterative training and testing loop to predict a target variable; and repeatedly predicting a value for the target variable during a plurality of iterations of the training and testing loop, each iteration of the plurality of iterations having differing weights applied to one or more nodes of the machine learning model, each of the differing weights being updated with each respective iteration of the training and testing loop to reduce an error in predicting the value for the target variable for remaining iterations; deploy the machine learning model; …using the deployed machine learning model;… using the deployed machine learning model; update and tune the deployed machine learning model as new data about the financial wellness of the person is collected and processed. (Claim 2 and 16);
…a client central database that stores name, address, birthdate, account types, account balances, social security number and credit scores for clients of the bank (Claims 4 and 17);
…stores information and data (Claims 5 and 17);
the at least one processor (Claims 4-8, 12, and 14);
…from at least one wearable device being worn by the person (Claims 8 and 18);
wherein the at least one wearable device (Claims 9 and 19);
wherein the at least one wearable device is one of a fitness tracker, a smart watch, a connected headset, smart glasses or a wrist band (Claim 10);
wherein the at least one wearable device (Claim 11).
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use 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.
Independent claims and dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use 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. For example, independent claims and dependent claims are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same. See MPEP § 2106.
In Step 2A, several additional elements were identified as additional limitations:
A system for determining a financial wellness of a person over time, said system comprising: a back-end server including: at least one processor; a communications interface communicatively coupled to the at least one processor; and a memory device storing executable code that, when executed, causes the at least one processor to: (Claim 2);
…,said collected data being stored as ingested data, wherein the at least one processor…; transform the ingested data from a plurality of data formats into a standardized training format for training a machine learning model; train, using training test data of the ingested data the machine learning model to …; the training including: inserting the training data into an iterative training and testing loop to predict a target variable; and repeatedly predicting a value for the target variable during a plurality of iterations of the training and testing loop, each iteration of the plurality of iterations having differing weights applied to one or more nodes of the machine learning model, each of the differing weights being updated with each respective iteration of the training and testing loop to reduce an error in predicting the value for the target variable for remaining iterations; deploy the machine learning model; …using the deployed machine learning model;… using the deployed machine learning model; update and tune the deployed machine learning model as new data about the financial wellness of the person is collected and processed. (Claim 2 and 16);
…a client central database that stores name, address, birthdate, account types, account balances, social security number and credit scores for clients of the bank (Claims 4 and 17);
…stores information and data (Claims 5 and 17);
the at least one processor (Claims 4-8, 12, and 14);
…from at least one wearable device being worn by the person (Claims 8 and 18);
wherein the at least one wearable device (Claims 9 and 19);
wherein the at least one wearable device is one of a fitness tracker, a smart watch, a connected headset, smart glasses or a wrist band (Claim 10);
wherein the at least one wearable device (Claim 11).
These additional limitations, including the limitations in the independent claims and dependent claims, do not amount to an inventive concept because the recitations above do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use 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. In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
For these reasons, the claims are rejected under 35 U.S.C. 101.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 2, 4-5, 8-14, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Chintakindi et al. (US Pub. No. 20230230121 A1) in view of Douthit et al. (US Patent No. 11928569 B1, hereinafter “Douthit”) and further in view of Sanghvi et al. (US Pub. No. 20220036450 A1, hereinafter “Sanghvi”).
Regarding claims 2 and 16
A system for determining a financial wellness of a person over time, wherein the financial wellness is a combination of a financial wellbeing and a physical wellbeing of the person, wherein a bank is determining the financial wellness of the person and the person is a client of the bank, said system comprising: a back-end server including: at least one processor; a communications interface communicatively coupled to the at least one processor; and a memory device storing executable code that, when executed, causes the at least one processor to (Chintakindi, [0043]: processor, memory, and server; [0072]: processor uses machine learning; [0128]: physical and financial data; [0429]: financial transaction data to provide picture of user’s wellness or health; [0430]: data includes health records and data from financial institutions; [0431]: improving the health of the user, may also be associated with a savings; [0110]: insurance rate based on mortality rate; [0396]: life insurance; [0467]: using machine learning to analyze financial data and output financial recommendations and insights such as an offer; [0469]: user insights identified from sources (e.g. particular bank); [0487]: the sources may include a particular bank or financial institution associated with one or more credit or debit cards of the user; [0119]: platform may be associated with financial institution):
collect data as it is being received over time about the financial wellness of the person, said collected data being stored as ingested data, wherein the at least one processor collects the data about the financial wellness of the person from a financial monitoring source …and from a money and a mindset source that provides a determination of a financial literacy of the person (Chintakindi, [0103]: receive registration response data from user device; [0104]: transmit user data to be analyzed; [0105]: analyze data using machine learning to calculate score or rating; [0228]: retrieve user data in real-time; [0108]: capture and process data related to financial transactions; [0113]: financial transaction data includes data from bank; [0429]: retrieve financial transaction data to provide picture of user’s wellness or health; [0128]: physical and financial data; [0429]: financial transaction data to provide picture of user’s wellness or health; [0431]: improving the health of the user, may also be associated with a savings; [0110]: insurance rate based on mortality rate; [0396]: life insurance; [0495]: data received from various sources such as a third party storing financial transaction data or purchase history data; [0487]: the sources may include a particular bank or financial institution associated with one or more credit or debit cards of the user; [0119]: platform may be associated with financial institution; [0311]: process all data; [0079]: data obtained and/or generated by any of these devices, as well as various devices described below, can be stored; [0069]: one or more databases 212 configured to store data associated with a user; [0490]: data related to amounts spent on dining in, dining out, subscription services, groceries, average spent on dining in or out, food delivery fees, days of the week on which the most is spent; [0467]: using machine learning to analyze financial data and output financial recommendations and insights such as an offer for products or services);
perform data cleaning on the ingested data; transform the ingested data from a plurality of data formats into a standardized training format for training a machine learning model (Chintakindi, [0111] the data may be formatted from a format in which it was transmitted such as data that may be filtered and/or portions removed, deleted; [0125]: data may be may be further formatted, converted, or the like, prior to processing; [0130]: the data may be received from the various sources in different formats. In some examples, the formats may be structured data formats and/or unstructured data formats; [0332]: data may be downloaded in one or more formats; [0522]: data may be ingested as training data and annotated and/or labeled for ease of classification); [0142]: training data may be received from one or more users and used to generate machine learning datasets and update and/or validate the machine learning datasets);
….the machine learning model to predict factors for determining the financial wellness of the person, … (Chintakindi, [0522]: data may be ingested as training data; [0467]: machine learning processes received data to output predictions based on factors related to health, wellness, and financial data; [0429]: financial transaction data to provide picture of user’s wellness or health; [0431]: improving the health of the user, may also be associated with a savings; [0110]: insurance rate based on mortality rate; [0396]: life insurance; [0110]: insurance rate based on mortality rate; [0396]: life insurance recommendations),
process the collected data as it is being received over time using the … machine learning model; determine the financial wellness of the person based on the processed data using the … machine learning model (Chintakindi, [0103]: receive registration response data from user device; [0104]: transmit user data to be analyzed; [0105]: analyze data using machine learning to calculate score or rating; [0228]: retrieve user data in real-time; [0108]: capture and process data related to financial transactions; [0113]: financial transaction data includes data from bank; [0429]: retrieve financial transaction data to provide picture of user’s wellness or health; [0128]: physical and financial data; [0429]: financial transaction data to provide picture of user’s wellness or health; [0431]: improving the health of the user, may also be associated with a savings; [0110]: insurance rate based on mortality rate; [0396]: life insurance;;
and update and tune the… machine learning model as new data about the financial wellness of the person is collected and processed (Chintakindi, [0147]: machine learning datasets 712g may be updated and/or validated based on later-received data based on the newly received information and continuously refined; [0143]: machine learning engine generates machine learning datasets; [0431]: machine learning may be used to evaluate the data and generate one or more recommendations for improved health of the user associated with a savings; [0467]: using machine learning to analyze financial data and output insights including financial; [0110]: insurance rate based on mortality rate; [0396]: life insurance recommendations).
Chintakindi does not teach:
…that provides significant credit score changes, changes in direct deposit patterns or income changes including loss of employment and reduction in work hours, changes in transaction and account patterns including account closures, frequent overdrafts, late payments and sudden increase in debt-related transactions, changes in credit card patterns including increased transaction frequency for basic needs with decreased spending in dining out and entertainment, sale of investments or assets, requests for payment extensions or loan modifications and payday loans or cash advances…
train, using training test data of the ingested data …. the training including: inserting the training data into an iterative training and testing loop to predict a target variable; and repeatedly predicting a value for the target variable during a plurality of iterations of the training and testing loop, each iteration of the plurality of iterations having differing weights applied to one or more nodes of the machine learning model, each of the differing weights being updated with each respective iteration of the training and testing loop to reduce an error in predicting the value for the target variable for remaining iterations; deploy the machine learning model;… using the deployed machine learning model; …using the deployed machine learning model; …the deployed machine learning model.
However, Douthit teaches:
train, using training test data of the ingested data …. the training including: inserting the training data into an iterative training and testing loop to predict a target variable; and repeatedly predicting a value for the target variable during a plurality of iterations of the training and testing loop, each iteration of the plurality of iterations having differing weights applied to one or more nodes of the machine learning model, each of the differing weights being updated with each respective iteration of the training and testing loop to reduce an error in predicting the value for the target variable for remaining iterations; deploy the machine learning model;… using the deployed machine learning model; …using the deployed machine learning model; …the deployed machine learning model (Douthit, C13, L4-67: training model using training data including a label that includes one or more sequence of tokens and output sequence of tokens to achieve a correct result which has a high likelihood and compare the output to labels to indicate a degree of association with that label including a string, numerical value or binary value associated with one or more labels. From comparing labels, proceed to adjusting parameters associated with tokens of the model to improve accuracy of model and determine measure of error between training iterations based on threshold amount and conditions; C14, L1-27: training of model is iterated and parameters adjusted during training include weights used by nodes in neural network and comparison and testing of models are performed and models deployed to output in response to receiving input; C6, L35-67: predictive model provides likelihood of correctness).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the machine learning model of Chintakindi with iterative training and testing loop, different weights assigned to nodes, deploying the machine learning model as taught by Douthit because the results of such a modification would be predictable. Specifically, Chintakindi would continue to teach the machine learning model except that now iterative training and testing loop, different weights assigned to nodes, deploying the machine learning model is taught according to the teachings of Douthit in order to improve accuracy of the model. This is a predictable result of the combination. (Douthit, C13, L50-67).
However, Sanghvi teaches:
…that provides significant credit score changes, changes in direct deposit patterns or income changes including loss of employment and reduction in work hours, changes in transaction and account patterns including account closures, frequent overdrafts, late payments and sudden increase in debt-related transactions, changes in credit card patterns including increased transaction frequency for basic needs with decreased spending in dining out and entertainment, sale of investments or assets, requests for payment extensions or loan modifications, and payday loans or cash advances (Sanghvi, [0034]: detect decrease in customer credit score, loans, and purchase activities; [0033]: changes in income deposits associated with employment status, payment patterns, account activity, payments that are delayed, overdue, missed; [0037]: decrease in deposits; [0031]: consumer behavior, income activity, spending activity, credit rating patterns, loan activity, risk appetites; [0036]: deviation in payment; [0038]: determine a large deviation from a pattern of activity).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the collection of data of Chintakindi and Douthit with significant credit score changes, changes in direct deposit patterns or other financial data as taught by Sanghvi because the results of such a modification would be predictable. Specifically, Chintakindi and Douthit would continue to teach the collection of data except that now significant credit score changes, changes in direct deposit patterns, or other financial data is taught according to the teachings of Sanghvi in order to detect deviations from the pattern of the user activity. This is a predictable result of the combination. (Sanghvi, [0008-0010]).
Regarding claim 4
The combination of Chintakindi, Douthit, and Sanghvi teaches the system according to claim 2 wherein the at least one processor collects the data about the financial wellness of the person from a client central database that stores name, address, birthdate, account types, …, social security number … (Chintakindi, [0103]: receive registration response data from user device (e.g. name, contact information); [0104]: transmit user data to be analyzed; [0108]: capture and process data related to financial transactions; [0113]: financial transaction data includes data from bank; [0429]: retrieve financial transaction data to provide picture of user’s wellness; [0300]: user's name, address, date of birth; [0049]: credit history; [0079]: data obtained and/or generated by any of these devices, as well as various devices described below, can be stored; [0069]: one or more databases 212 configured to store data associated with a user; [0487]: the sources may include a particular bank or financial institution associated with one or more credit or debit cards of the user; [0130]: personally identifiable information (PII)).
However, Sanghvi teaches:
…account balances …and credit scores for clients of the bank (Sanghvi, [0034]: detect customer credit score; [0022]: account balances; [0024]: banking).
The motivation to combine Chintakindi and Douthit with Sanghvi is the same as set forth above in claim 1.
Regarding claim 5
The combination of Chintakindi, Douthit, and Sanghvi teaches the system according to claim 2 wherein the at least one processor collects the data about the financial wellness of the person from a client interaction and transaction source that stores information and data obtained for each of the interactions and transactions between all of the banks clients and the bank over all banking channels (Chintakindi, [0103]: receive registration response data from user device (e.g. name, contact information); [0104]: transmit user data to be analyzed; [0108]: capture and process data related to financial transactions; [0113]: financial transaction data includes data from bank; [0481]: the data may include one or more of location data, purchase history data, financial transaction data; [0495]: data received from various sources such as a third party storing financial transaction data or purchase history data; [0487]: the sources may include a particular bank or financial institution associated with one or more credit or debit cards of the user; [0119]: platform may be associated with financial institution; [0311]: process all data; [0079]: data obtained and/or generated by any of these devices, as well as various devices described below, can be stored; [0069]: one or more databases 212 configured to store data associated with a user; [0490]: data related to amounts spent on dining in, dining out, subscription services, groceries, average spent on dining in or out, food delivery fees, days of the week on which the most is spent).
Regarding claims 8 and 18
The combination of Chintakindi, Douthit, and Sanghvi teaches the system according to claim 2 wherein the at least one processor collects the data about the financial wellness of the person from at least one wearable device being worn by the person (Chintakindi, [0097]: collect user data from wearable device; [0246]: generated insights may include insights based on wearable device data; [0349]: user data includes financial transaction data; [0429]: retrieve financial transaction data to provide picture of user’s wellness).
Regarding claims 9 and 19
The combination of Chintakindi, Douthit, and Sanghvi teaches the system according to claim 8 wherein the at least one wearable device detects one or more of heart rate, respiration rate, changes in skin condition, such as from sweating and temperature changes, alterations in sleep patterns, such as difficulty in sleeping and restlessness, blood pressure, diet-related factors, muscle tension and pain, body temperature, oxygen saturation and blood sugar (Chintakindi, [0056]: devices include wearable devices such as smart watches and fitness monitors; [0107]: wearable device captures user physical data such as heart rate, blood pressure, fitness or activity data, oxygen capacity, pulse; [0491]: sleep; [0441]: diabetes and disease).
Regarding claim 10
The combination of Chintakindi, Douthit, and Sanghvi teaches the system according to claim 8 wherein the at least one wearable device is one of a fitness tracker, a smart watch, a connected headset, smart glasses or a wrist band (Chintakindi, [0387]: wearable device such as a fitness tracker, smart watch, or the like).
Regarding claim 11
The combination of Chintakindi, Douthit, and Sanghvi teaches the system according to claim 8 wherein the at least one wearable device is provided by the bank or provided by a third-party or both (Chintakindi, [0488]: the source(s) of data may include a fitness tracker or other wearable device, health and wellness tracking application executing on a mobile device of a user; [0487]: the sources of data may include a particular bank or financial institution).
Regarding claim 12
The combination of Chintakindi, Douthit, and Sanghvi teaches the system according to claim 2 wherein the at least one processor collects the data about the financial wellness of the person from an image of the person (Chintakindi, [0371]: user information response data may be received and/or captured by the remote user computing device 770. For instance, a user may capture one or more images of himself or herself; [0372]: the user information response data may be received by the offer generation computing platform 710 and, with reference to FIG. 18E, at step 1823, the user information response data may be processed by the offer generation computing platform; [0395]: user information response data received may be analyzed to determine overall health).
Regarding claim 13
The combination of Chintakindi, Douthit, and Sanghvi teaches the system according to claim 2 wherein the bank provides recommendations for bank products and/or services based on the financial wellness of the person (Chintakindi, [0508]: the offer or incentive may include a payment of cash or other funds to the user. Accordingly, a deposit of the identified funds may be deposited in a user account (e.g., as provided by the user); [0467]: using machine learning to analyze financial data and output financial recommendations and insights such as an offer for products or services; [0469]: user insights identified from sources (e.g. particular bank); [0487]: the sources may include a particular bank or financial institution associated with one or more credit or debit cards of the user; [0119]: platform may be associated with financial institution; [0429]: retrieve financial transaction data to provide picture of user’s wellness or health).
Regarding claim 14
The combination of Chintakindi, Douthit, and Sanghvi teaches the system according to claim 13 wherein the at least one processor continuously determines and updates the financial wellness of the person including after the person has implemented one or more of the recommendations for the bank products and/or services (Chintakindi, [0142]: training data may be received from one or more users and used to generate machine learning datasets and update and/or validate the machine learning datasets; [0143]: machine learning engine uses neural network; [0467]: using machine learning to analyze financial data and output financial recommendations and insights such as an offer for products or services; [0232]: additional data may be captured after a user has accepted a generated offer; [0147]: machine learning datasets 712g may be updated and/or validated based on later-received data based on the newly received information (e.g. as additional data collected from subsequent offer requests) and continuously refined).
Regarding claim 17
The combination of Chintakindi, Douthit, and Sanghvi teaches the method according to claim 16 wherein the method also
collects the data about the financial wellness of the person from: a client central database that stores name, address, birthdate, account types, …, social security number …(Chintakindi, [0103]: receive registration response data from user device (e.g. name, contact information); [0104]: transmit user data to be analyzed; [0108]: capture and process data related to financial transactions; [0113]: financial transaction data includes data from bank; [0429]: retrieve financial transaction data to provide picture of user’s wellness; [0300]: user's name, address, date of birth; [0049]: credit history; [0079]: data obtained and/or generated by any of these devices, as well as various devices described below, can be stored; [0069]: one or more databases 212 configured to store data associated with a user; [0487]: the sources may include a particular bank or financial institution associated with one or more credit or debit cards of the user; [0130]: personally identifiable information (PII););
a client interaction and transaction source that stores information and data obtained for each of the interactions and transactions between all of the banks clients and the bank over all banking channels (Chintakindi, [0103]: receive registration response data from user device (e.g. name, contact information); [0104]: transmit user data to be analyzed; [0108]: capture and process data related to financial transactions; [0113]: financial transaction data includes data from bank; [0481]: the data may include one or more of location data, purchase history data, financial transaction data; [0495]: data received from various sources such as a third party storing financial transaction data or purchase history data; [0487]: the sources may include a particular bank or financial institution associated with one or more credit or debit cards of the user; [0119]: platform may be associated with financial institution; [0311]: process all data; [0079]: data obtained and/or generated by any of these devices, as well as various devices described below, can be stored; [0069]: one or more databases 212 configured to store data associated with a user; [0490]: data related to amounts spent on dining in, dining out, subscription services, groceries, average spent on dining in or out, food delivery fees, days of the week on which the most is spent);
However, Sanghvi teaches:
…account balances …and credit scores for clients of the bank (Sanghvi, [0034]: detect customer credit score; [0022]: account balances; [0024]: banking).
The motivation to combine Chintakindi and Douthit with Sanghvi is the same as set forth above in claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is cited as Costa et al. (US Patent No. 12361480 B1) related to financial health automated advising, Gilliam et al. (US Pub. No. 20200258158 A1) related to tracking the spending and saving habits of users to offer incentives, and non-patent literature, “Financial health as a measurable social determinant of health,” related to the financial health relevant to overall well-being by examining the relationship between components of financial health, depression and self-rated health.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LATASHA DEVI RAMPHAL whose telephone number is (571)272-2644. The examiner can normally be reached 11 AM - 7:30 PM (EST).
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/LATASHA D RAMPHAL/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
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