DETAILED ACTION
This rejection is in response to Amendments filed 11/11/2025.
Claims 2-14 and 16-19 are currently pending and have been examined.
Claims 1, 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 .
Response to Arguments
Applicant’s arguments, see page 1, filed 11/11/2025, with respect to 35 U.S.C. 112(b) rejection (e.g. a memory device storing data, collect data and information, the collected data; the processed data; the financial literacy) to claims 1-20 have been fully considered and are persuasive. The 35 U.S.C. 112(b) to claims 1-20 has been withdrawn.
Applicant's arguments filed 11/11/2025 have been fully considered but they are not persuasive.
With respect to applicant’s arguments on page 1-2 of remarks filed 11/11/2025 that the non-statutory double patenting rejection has been overcome by the enclosed terminal disclaimer, Examiner respectfully disagrees.
A terminal disclaimer does not appear to be filed or approved with the other documents that were filed on 11/11/2025. Therefore, the non-statutory double patenting rejection has not been withdrawn since the terminal disclaimer was not filed.
With respect to applicant’s arguments on page 2-7 of remarks filed 11/11/2025 the claims are not directed to certain methods of organizing human activity because the claim recites training a model, collecting data, and using the data to predict factors for determining a financial wellness of a person 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, testing and comparing data, indicating modifications, and updating data and information. The neural network model that is trained is not considered directed towards certain methods of organizing human activity.
The instant claims recite functions of a neural network (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 7-13 of remarks filed 11/11/2025 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 predictability of the target variable and functionality of the neural network 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 merely improving a prediction factor improves technology or the neural network. 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 functionality of the neural network. Applicant’s specification describes what a neural network generally includes and does but does not provide further detail as to how the neural network 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.
Therefore, the additional elements (e.g. server, memory device, processor, neural network) 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 to prevent network instructions 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 13-15 of remarks filed 11/11/2025 that the combination of limitations are not well-understood, routine, or generic because the claims recite determining the financial wellness of a person based on the processed data and information using the trained and deployed neural network, 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 15-17 of remarks filed 11/11/2025 that Chintakindi and Sanghvi do not teach that the financial wellness is a combination of a financial wellbeing and a physical wellbeing because Chintakindi only collects and aggregates information to evaluate a person’s health, 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])
Non-Statutory Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2-14 and 16-19 of the instant claims are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/624,389 in view of Chintakindi et al (US Pub. No. 20230230121 A1) and Douthit et al. (US Patent No. 11928569 B1, hereinafter “Douthit”).The reference claims of copending Application No. 18/624,389 recite all of the limitations recited in the instant claims, except for training a neural network, iteratively predicting factors, testing and comparing, indicating modifications, deploying the trained neural network, using the deployed neural network, and updating and tuning the deployed neural network in independent claims 2 and 16. However, Chintakindi teaches updating and tuning the … model as new data and information about the factor of the unit is collected and processed. (Chintakindi, [0142-0143] and [0147]). It would have been obvious to one of ordinary skill in the art to have modified the machine learning model recited in the reference claims with updating and tuning the model as taught by Chintakindi in order to update the model with newly received data. (Chintakindi, [0142-0143] and [0147]). Douthit teaches training a neural network, iteratively predicting factors, testing and comparing, indicating modifications, deploying the trained neural network, using the deployed neural network (Douthit, C13, L4-67; C14, L1-27). 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 the reference claims with training a neural network to predict factors including iteratively predicting factors, generating a prediction, testing and comparing, indicating modifications to weights assigned to nodes, deploying the neural network, and using the deployed neural networks as taught by Douthit in order to improve accuracy of the model. (Douthit, C13, L50-67).
This is a provisional nonstatutory double patenting rejection.
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 2-14 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: collect data and information …; process the collected data and information …; determine the financial wellness of the person based on the processed data and information…; and update and tune …as new data and information …, rendering said claims indefinite because it is unclear whether the first recitation of information is the same or different from the subsequent recitations of information. Appropriate correction or clarification is required.
Claims 2 and 16 recites: [a] system for determining a financial wellness of a person …; train, using training test data, a neural network to predict factors for determining a financial wellness of the person, rendering said claims indefinite because it is unclear whether the first recitation of a financial wellness is the same or different from the subsequent recitations of a financial wellness. Appropriate correction or clarification is required.
Claims 5-8, 12, and 17-18 recite data and information, rendering said claims indefinite because it is unclear whether the first recitation of data and information in independent claims 2 and 16 are the same or different from the subsequent recitations of data and information in claims 5-8, 12, and 17-18. 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 2-14 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 2-14 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…;
…to predict factors for determining a financial wellness of the person, the training including:
iteratively predicting the factors from the training test data that correlate to the financial wellness of the person, the predicting generating a prediction;
testing and comparing, during each iteration, the prediction to a target variable;
indicating, for each iteration and via a feedback loop, modifications to weights assigned to nodes …to predict the target variable and reduce error of the prediction;
collect data and information as it is being received over time about the financial wellness of the person;
process the collected data and information 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 includes determining the financial wellness of a person by collecting and processing data, predicting factors for determining financial wellness, testing and comparing data, indicating modifications, and updating data and information.
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);
…train, using training test data, a neural network…; …of the neural network to improve the neural network’s ability …; deploy the trained neural network…;…using the deployed neural network…;… using the deployed neural network;…the deployed neural network (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);
…train, using training test data, a neural network…; …of the neural network to improve the neural network’s ability …; deploy the trained neural network…;…using the deployed neural network…;… using the deployed neural network;…the deployed neural network (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) 2-5, 7-14, 16, and 18-19 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”).
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, 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):
….for determining a financial wellness of the person, …;….to the financial wellness of the person, …collect data and information as it is being received over time about the financial wellness of the person; process the collected data and information as it is being received over time using the…model; determine the financial wellness of the person based on the processed data and information using the…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; [0110]: insurance rate based on mortality rate; [0396]: life insurance recommendations);
and update and tune the…model as new data and information 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:
train, using training test data, a neural network to predict factors…;
the training including: iteratively predicting the factors from the training test data that correlate…; the predicting generating a prediction; testing and comparing, during each iteration, the prediction to a target variable; indicating, for each iteration and via a feedback loop, modifications to weights assigned to nodes of the neural network to improve the neural network’s ability to predict the target variable and reduce error of the prediction; deploy the trained neural network;…using the deployed neural network model; …using the deployed neural network model; … the deployed neural network model…
However, Douthit teaches:
train, using training test data, a neural network to predict factors…(Douthit, C5, L35-67: training predictive model (e.g. neural network) based on labeled training data to provide likelihood for sequence of tokens);
the training including: iteratively predicting the factors from the training test data that correlate…; the predicting generating a prediction; testing and comparing, during each iteration, the prediction to a target variable; indicating, for each iteration and via a feedback loop, modifications to weights assigned to nodes of the neural network to improve the neural network’s ability to predict the target variable and reduce error of the prediction; deploy the trained neural network; …using the deployed neural network model; …using the deployed neural network model; … the deployed neural network 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);
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 neural network of Chintakindi with training a neural network to predict factors including iteratively predicting factors, generating a prediction, testing and comparing, indicating modifications to weights assigned to nodes, deploying the neural network, and using the deployed neural networks as taught by Douthit because the results of such a modification would be predictable. Specifically, Chintakindi would continue to teach the neural network except that now training a neural network to predict factors including iteratively predicting factors, generating a prediction, testing and comparing, indicating modifications to weights assigned to nodes, deploying the neural network, and using the deployed neural network 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).
Regarding claim 16
Claim 16 is substantially similar to claim 2 but recites additional limitations (e.g. a person who is a client of a bank). (Chintakindi [0469] and [0487]).
Regarding claim 3
The combination of Chintakindi and Douthit teaches the system according to claim 2 wherein a bank is determining the financial wellness of the person and the person is a client of the bank (Chintakindi, [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).
Regarding claim 4
The combination of Chintakindi and Douthit teaches the system according to claim 3 wherein the at least one processor collects data and information about the financial wellness of the person from a client central database that stores name, address, birthdate, account types, account balances, social security number and credit scores for clients of the bank (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); [0083]: a balance of the account).
Regarding claim 5
The combination of Chintakindi and Douthit teaches the system according to claim 3 wherein the at least one processor collects data and information 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 claim 7
The combination of Chintakindi and Douthit teaches the system according to claim 3 wherein the at least one processor collects data and information about a financial wellness of the person from a money and mindset source that provides a determination of the financial literacy of the person (Chintakindi, [0518]: analyze purchase history and other data to assess user (e.g. user with low credit score viewed as credit risk); [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).
Regarding claims 8 and 18
The combination of Chintakindi and Douthit teaches the system according to claim 3 wherein the at least one processor collects data and information 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 and Douthit 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 and Douthit 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 and Douthit 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 and Douthit teaches the system according to claim 3 wherein the at least one processor collects data and information 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 and Douthit teaches the system according to claim 3 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 and Douthit 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).
Claim(s) 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chintakindi and Douthit as applied to independent claims 2 and 16 above, and further in view of Sanghvi et al. (US Pub. No. 20220036450 A1, hereinafter “Sanghvi”).
Regarding claim 6
The combination of Chintakindi and Douthit teaches the system according to claim 3 wherein the at least one processor collects data and information about the financial wellness of the person from a financial monitoring source… (Chintakindi, [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)
However, Chintakindi and Douthit do 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.
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 and information 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 and information 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 17
The combination of Chintakindi and Douthit teaches the method according to claim 16 wherein the method
collects data and information about the financial wellness of the person from one or more of: a client central database that stores name, address, birthdate, account types, account balances, social security number and credit scores for clients of the bank (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); [0083]: a balance of the account);
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; a financial monitoring source … (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);
and a money and mindset source that provides a determination of the financial literacy of the person (Chintakindi, [0518]: analyze purchase history and other data to assess user (e.g. user with low credit score viewed as credit risk); [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).
Chintakindi and Douthit do 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;
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).
The motivation to combine Chintakindi and Douthit with Sanghvi is the same as set forth above in claim 6.
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.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey A. Smith can be reached at 5712726763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LATASHA D RAMPHAL/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688