DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Applicant’s election without traverse of Species VIII in the reply filed on February 26, 2026 is acknowledged. Generic claims 1, 2 and 15 and Species VIII (claims 3-7 and 16) are examined in this non-final office in response to the species election. Non-elected claims 8-14 and 17-20 are withdrawn from examination.
In a formal reply, please consider identifying the status of non-elected claims as “Withdrawn” rather than “Canceled.” Any amendments to examined claims may require amendments to withdrawn claims. The undersigned examiner is suggesting the withdrawn claims be amended if necessary.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 112
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.
Claim 7 is 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.
“Mindset” is subjective and renders the claim indefinite. Correction is requested.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7, 15 and 16 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without adding significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to either a practical application of the abstract idea or significantly more than the abstract idea itself. Groupings of abstract ideas include: Mathematical Concepts, Mental Processes and Certain Methods of Organizing Human Activity.
Certain Methods of Organizing Human Activity include:
Fundamental economic principles or practices,
Commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), and
Managing personal behavior or relationships or interaction between people (including social activities, teaching and following rules or instructions).
Mathematical Concepts
Mathematical relationships
Mathematical formulas
Mathematical calculations
Mental Processes
Concepts performed in the human mind (including an observation, evaluation, judgement, opinion)
Step 1
In the instant case, claim 2 is directed to a process. Analysis of claim 2 applies to analysis of claims 1, 3-7, 15 and 16
Step 2A Revised (First Prong)
Determine whether claim 2 is directed to a judicial exception. Elements of an abstract idea are underlined. See Analysis.
Step 2A Revised (Second Prong)
Determine whether claim 2 has additional elements (in italics) integrated into a practical application:
a) requires an additional element or a combination of elements in the claim to apply, rely on, or use the judicial exception in a manger that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception; and
b) uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application.
See Analysis.
Step 2B (Revised)
In Step 2B, evaluate whether claim 2 recites additional elements that amount to an inventive concept that adds significantly more than the recited judicial exception. See Analysis.
Analysis
In Claim 2:
A system for determining a financial wellness of a person over time, said system comprising: a back-end server including:
at least one processor for processing data and information, wherein the at least one processor employs a machine learning model;
a communications interface communicatively coupled to the at least one processor; and
a memory device storing data and executable code that, when executed, causes the at least one processor to:
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 machine learning model;
determine the financial wellness of the person based on the processed data and information; and
provide recommendations for bank products and/or services based on the financial wellness of the person.
Claim 2 executes methods that are directed to abstract ideas comprising processes that can be executed by a human while following a procedure that organizes human activity related to commercial interactions using conventional computing elements.
No evidence of an improvement to the functioning of a computer, or to any other technology or technical field.
No evidence exists in the instant specification or claims of a particular machine.
No evidence exists of a transformation or reduction of a particular article to a different state or thing.
The claim does not go beyond generally linking the use of the judicial exception to a particular technological environment, e.g. processor, device.
Claim 2 does not recite additional elements that amount to inventive concepts that are “significantly more” than the recited judicial exception. “Machine learning model” relies on conventional computer processing functions. Courts have routinely found conventional computer processing functions (e.g. sending/receiving data, formatting data, storing data, retrieving data, manipulating data, calculating, searching data, displaying data, organizing data) insignificant to transform an abstract idea into a patent-eligible invention. See Alice, 134 S. Ct. at 2360. As such, the claims amount to nothing significantly more than an instruction to implement the abstract idea across a generic computer network which is not enough to transform an abstract idea into a patent-eligible invention.
The elements of the instant process, when taken in combination, together do not offer substantially more than the sum of the functions of the steps when each is taken alone. That is, the steps involved in the recited process undertake their roles in performance of their activities according to their generic functionalities which are well-understood, routine and conventional. The elements together execute in routinely and conventionally accepted coordinated manners and interact with their partner elements to achieve an overall outcome which, similarly, is merely the combined and coordinated execution of generic computer functionalities which are well-understood, routine and conventional activities previously known to the industry.
Conclusion
Accordingly, the examiner concludes there are no meaningful limitations in claims 1-7, 15 and 16 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5, 15 and 16 are rejected under 35 USC 102(a)(1) as being anticipated by Martin et al., US 2009/0276368 “Martin.”
Martin teaches all the limitations of claims 1-3, 5, 15 and 16. In Martin see at least (underlined text is for emphasis):
Regarding claim 2: A system for determining a financial wellness of a person over time, said system comprising:
a back-end server including:
at least one processor for processing data and information, wherein the at least one processor employs a machine learning model;
[Martin: 0063] The system 800 and the recommender engine 802 can be implemented on any number of computer systems, for use by one or more users, including the exemplary system 900 shown in FIG. 9. Referring to FIG. 9, the system 900 includes one or more general purpose or personal computers, e.g., user computers 908A, 908B, … 908N, server 904, or worker computers 906A, 906B, . . . , 906N, that execute one or more instructions of one or more application programs or modules stored in corresponding system memory (not shown).
[Martin: 0065] The one or more general purpose or personal computers, e.g., user computers 908A, 908B, . . . , 908N, server 904, or worker computers 906A, 906B, . . . , 906N comprise a processor or processing unit 952, memory 950, device interface 958, and network interface 960, all interconnected through a bus 954. Each of the user computers 908A, 908B, . . . , 908N, server 904, or worker computers 906A, 906B, . . . , 906N can include a single or multiple processors 952. Each of the user computers 908A, 908B, . . . , 908N, server 904, or worker computers 906A, 906B, . . . , 906N can utilize the advantages offered by a distributed system in which available processing power in the one or more processors 952 in one or more of the computers is used by others of the computers. Each of the user computers 908A, 908B, . . . , 908N, server 904, or worker computers 906A, 906B, . . . , 906N can include one or more memory devices 950 including random access memory (RAM) or read only memory (ROM). The memory devices may include a basic input/output system (BIOS) 950A with routines to transfer data between the various elements of the computer system 900. The memory 950 may also include an operating system (OS) 950B that, after being initially loaded by a boot program, manages all the other programs in each of the user computers 908A, 908B, . . . , 908N, server 904, or worker computers 906A, 906B, . . . , 906N. These other programs may be, e.g., application programs 950C. The application programs 950C make use of the OS 950B by making requests for services through a defined application program interface (API). In addition, users can interact directly with the OS 950B through a user interface such as a command language or a graphical user interface (GUI) (not shown). In one embodiment, the recommender engine 802, the financial products fetcher 804, tips fetcher 808, the mining and analysis component 806, or combinations thereof include one or more APIs implemented on one or more of the user computers 908A, 908B, . . . , 908N, server 904, or worker computers 906A, 906B, . . . , 906N.
[Martin: 0138] The sliding window 1502 converts sequential data into a form that is easy to use with many classical machine-learning techniques. More specifically, the product recommender 1302 includes one or more regression trees since they offer quick training times and transparency. In contrast to many machine learning models, the results of a regression tree are human understandable.
[Martin: 0145] In one embodiment, the regression trees 1302A-D can be part of the so-called Weka (Waikato Environment for Knowledge Analysis) machine learning software suite, developed at the University of Waikato in New Zealand. The Weka suite contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection.
a communications interface communicatively coupled to the at least one processor; and
[Martin: 0068] Network interface 960 operatively couples one computer, e.g., the computer 906A, to other computers, e.g., any of the user computers 908B, . . . , 908N, server 904, or worker computers 906A, 906B, . . . , 906N on a local or wide area network. Each of the user computers 908A, 908B, . . . , 908N, server 904, or worker computers 906A, 906B, . . . , 906N can be geographically local or remote from each other. Each of the user computers 908A, 908B, . . . , 908N, server 904, or worker computers 906A, 906B, . . . , 906N can have the structure of computer 906A, or may be a server, client, router, switch, or other networked device and typically includes some or all of the elements of computer 906. The computer 906A can connect to a local area network through a network interface or adapter included in the interface 960. The computer 906A may connect to a wide area network through a modem or other communications device included in the interface 960. The modem or communications device may establish communications to remote computers through global communications network 902. A person of reasonable skill in the art should recognize that application programs or modules 950C might be stored remotely through such networked connections.
a memory device storing data and executable code that, when executed, causes the at least one processor to:
[Martin: 0067] The hard disk drive 962, optical disk drive 964, magnetic disk drive 966, or like may include a computer readable medium that provides non-volatile storage of computer readable instructions of one or more application programs or modules 950C and their associated data structures. A person of skill in the art will recognize that the system 900 may use any type of computer readable medium accessible by a computer, such as magnetic cassettes, flash memory cards, digital video disks, cartridges, RAM, ROM, and like.
collect data and information as it is being received over time about the financial wellness of the person;
[Martin: 0137] The inputs are the same for each of those five training instances. The sliding window 1502 uses a three-month history period 1504 and a two-month target period 1506. The history period 1504 captures the user's recent activity, e.g., gross and net income, expenses, owned products, and recently purchased products.
process the collected data and information as it is being received over time using the machine learning model;
[Martin: 0138] The sliding window 1502 converts sequential data into a form that is easy to use with many classical machine-learning techniques. More specifically, the product recommender 1302 includes one or more regression trees since they offer quick training times and transparency. In contrast to many machine learning models, the results of a regression tree are human understandable.
[Martin: 0145] In one embodiment, the regression trees 1302A-D can be part of the so-called Weka (Waikato Environment for Knowledge Analysis) machine learning software suite, developed at the University of Waikato in New Zealand. The Weka suite contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection.
provide recommendations for bank products and/or services based on the financial wellness of the person.
[Martin: 0031] FIGS. 2A-C are screen shots associated with an embodiment of an Accounts widget 202 and 204 and its dialog box 200. In an embodiment, clicking on the Accounts widget dialog box 200 in the widget section 104 (FIG. 1) opens the widget 202 (or 204) to display an overview of the user's accounts as shown in FIG. 2B. Embodiments of the systems and methods allow the user to automatically link all of his financial accounts and to refresh the accounts with the latest transactions. The Accounts widget 202 displays all of the user's accounts in detail including account number, financial institution, balance, transaction history, and the like. The user can customize the level of account detail displayed by the Accounts widget 202. FIG. 2C, for example, shows details 204 associated with the user's accounts at one particular financial institution BankInc.
[Martin: 0039] Database 810 contains data describing a variety of financial products and services. These products and services may be offered by banks or other financial institutions. In other words the data is supplied externally from such sources. A financial products fetcher application 804 may collect the financial products data using push or pull mechanisms, scraping the data from other online sources, or potentially from online search results. The financial products fetcher application 804 collects data or metadata describing financial products and services and stores the collected data in the database 810 so that it is available to the recommender engine 802. By way of example and not limitation, such products and services may include investments, such as certificates of deposits, securities, stocks bonds. Other products and services may include various types of bank accounts, savings accounts, credit cards, and the like.
[Martin: 0121] Product Recommender
[Martin: 0122] Referring back to FIG. 8, in an embodiment, the recommender engine 802 can include two individual recommenders, the product recommender 880 and the tag recommender 882. The product recommender 880 scores products, and more specifically, financial products, by predicting a user's interest over different financial products. The immediately following section describes the tag recommender 882. The engine 802 combines the results of the product and tag recommenders 880 and 882, respectively, to produce final user recommendations.
Regarding claim 3: Rejection is based upon the disclosures applied to claim 2 by Martin as recited above.
Regarding claim 5: Rejection is based upon the disclosures applied to claim 3 by Martin and by additional disclosures by Martin:
[Martin: 0043] Databases 820 and 822 represents a variety of raw data sources, e.g., raw financial transactions and raw product consumption data associated with a particular user of the website. This refers to the raw data representing user's financial transactions and financial product use. For example, the user's financial transactions may include all of the various debits, credits, transfers, or other transactions that occur in the user's bank accounts. The same type of data may be acquired for the user's others accounts such as savings accounts, investment accounts, credit card accounts, and the like. in addition, a user's financial transactions might include mortgages or other types of loans.
[Martin: 0044] User transaction data can be acquired in several ways. Some types of transactions may be entered by the user through the website user interface described earlier. Preferably, most financial transactions will be acquired by automatically periodically scrapping that data from external sources (not shown) using an application similar to the financial products fetcher. For example, the financial products fetcher can download a user's bank account transactions from the users bank website or internal databases, given the appropriate login credentials or other provisions to maintain security. Third party vendors are known, such as Yodlee, which are in the business of scraping financial data on behalf of users.
Regarding claims 1 and 15: Rejections are based upon the disclosures applied to claim 2 by Martin recited above:
Regarding claim 16: Rejection is based upon the disclosures applied to claims 2 and 15 by Martin recited above and dependent claim 7 reciting similar subject matter.
Claim Rejections - 35 USC § 103
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 4 is rejected under 35 USC 103 as being unpatentable over Martin, US 2009/0276368, in view Mozeika, US 2021/0082050.
Rejection is based in part upon the teachings applied to claim 2 by Martin and further upon the combination of Martin-Mozeika.
Martin illustrates in Fig. 2C: checking, credit and savings account types and balances. Martin further teaches: login name, social security payments, age (age determines birth year) and email or home address. Although Martin does not expressly mention a user’s social security number, user name and credit score, Mozeika on the other hand would have taught Martin other types of useful information.
In Mozeika see at least:
[Mozeika: 0089] … FLO score (analogous to a credit score). [Mozeika:0102] The access manager 306 can have instructions stored therein that, when executed by the processor(s) 302, enables the client device 102 to establish access to the FLO service system(s) 110, such as by setting up a user account with a user profile at the FLO service system(s) 110. In some cases, this can involve providing the FLO service system(s) identifying information about a user 104 for whom a user profile is to be set up. This information can include name, age, date of birth, social security number, address, email address, phone number, and/or other identifying information about the user 104.
It would have been obvious to one of ordinary skill in the art before the effective filing date to factor into financial planning and determining the user’s financial health is the user’s name, credit score and social security number.
Claims 6 and 7 are rejected under 35 USC 103 as being unpatentable over Martin, US 2009/0276368, in view D’Agostino, US 2025/0307290.
Regarding claim 7: Rejection is based in part upon the teachings applied to claim 3 by Martin and further upon the combination of Martin-D’Agostino.
In Martin see at least:
[Martin: 0026] In an embodiment, the inventive systems and methods automatically form user communities using any of a variety of system or user selected characteristics, including race, gender, profession, education, age, marital status, marriage, geographic location, home ownership, financial vehicles and institutions, and like. And the systems and methods may provide a means to show comparisons between the user and the rest of the community, while maintaining confidential the identities of the members of the community.
Although Martin does not expressly mention techniques for determining a user’s financial literacy, D’Agostino on the other hand would have taught Martin such techniques.
In D’Agostino see at least:
[D’Agostino: 0171] In one embodiment, the system introduces an emotionally intelligent chatbot designed for financial counseling purposes. Using advanced natural language processing (NLP) techniques and sentiment analysis algorithms, the chatbot accurately discerns users' emotional states and mood indicators during communication sessions. The chatbot can detect emotions such as stress, anxiety, optimism, or frustration by analyzing linguistic cues, tone of voice, and contextual information. Leveraging its understanding of users' emotions, the chatbot delivers empathetic and supportive responses tailored to users' emotional needs. The chatbot provides personalized financial guidance and advice based on users' emotional states, financial goals, and life circumstances. Whether users are navigating debt management, budgeting strategies, investment decisions, or retirement planning, the chatbot offers tailored recommendations and actionable insights aligned with users' emotional well-being and financial objectives. Additionally, the chatbot serves as a behavioral coach, helping users develop positive financial habits and attitudes. By reinforcing desirable behaviors, encouraging goal-setting, and providing motivational support, the chatbot empowers users to take control of their financial lives and make informed decisions that align with their long-term goals. The chatbot facilitates interactive learning experiences through quizzes, educational content, and interactive exercises designed to enhance users' financial literacy and skills. The chatbot leverages machine learning algorithms to continuously improve its understanding of users' emotional states, preferences, and conversational patterns. By learning from user interactions, feedback, and real-world experiences, the chatbot adapts and evolves its responses to better meet users' needs and deliver more effective support and guidance.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of D’Agostino, which a) facilitate via a chatbot interactive learning experiences through quizzes, educational content, and interactive exercises designed to enhance users' financial literacy and skills, and b) leverage the chatbot’s machine learning algorithms to continuously improve its understanding of users' emotional states, preferences, and conversational patterns, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of D’Agostino to the teachings of Martin would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Regarding claim 6: Rejection is based upon the teachings and rationale applied to the combination of Martin-D’Agostino and further upon the combination of Martin-D’Agostino regarding change detection in financial factors:
[D’Agostino: 0107] In one embodiment, the system analyzes communication patterns and behavioral cues within financial markets. It utilizes advanced linguistic models to analyze and interpret textual data, extracting valuable insights from various sources, including news articles, social media posts, and financial reports. The system extracts key information related to financial markets, companies, and economic indicators in real time. Utilizing LLMs identifies relevant keywords, sentiment indicators, and behavioral cues. The tool analyzes communication patterns and sentiment indicators and provides comprehensive insights into market sentiment. It identifies prevailing attitudes, emotions, and perceptions among investors, which can influence market dynamics and asset prices. It tracks investor sentiment by analyzing social media posts, forums, and other online discussions. It identifies emerging trends, hot topics, and investor sentiment shifts, allowing users to gauge market sentiment in real time. The tool uses machine learning algorithms to identify emerging trends and patterns within financial markets. It detects unusual market activity, significant news events, and changes in investor behavior, enabling users to stay ahead of market movements. The platform offers intuitive data visualization tools, allowing users to explore trends, patterns, and sentiment indicators visually. Users can gain deeper insights into market dynamics and investor behavior through interactive charts, graphs, and dashboards. Users can set up customizable alerts based on specific criteria, such as keyword mentions, sentiment shifts, or significant news events. This allows users to stay informed about relevant developments and take timely actions.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2019/0378207 (Dibner-Dunlap et al.) “Financial Health Tool,” discloses:
[0011] An example process for determining a user's financial health and personalizing their finances based on the financial health may proceed as follows. A system may gather data about a user's current and/or historical financial situation (e.g., debt, savings, investments, retirement plans, income pattern, spending pattern, resiliency to financial shock, etc.), data about the user's current and/or historical life situations (e.g., day-to-day life, household situation, life events, lifestyle, etc.), and/or data about the user's future expectations (e.g., wage increases, career advancement, social network changes, retirement strategy, etc.). Thus, the system may gather both financial data and personality data not directly related to finances (e.g., the life situation and future expectation data). The system may analyze the gathered data to determine the user's financial health. The financial health may therefore be based not only on finances, but also on the user's values, priorities, beliefs, personality, cognitive biases, etc. The system may reevaluate the data and adjust the financial health determination as the data changes in some embodiments. Based on the determined financial health, the system may personalize financial account optimizations (e.g., advice, tips, offers, products, etc.). This process, the system that performs the process, and variations thereon are described in detail below.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT M POND whose telephone number is (571)272-6760. The examiner can normally be reached M-F, 8:30 AM-6:30 PM.
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 Smith can be reached at 571-272-6763. 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.
/ROBERT M POND/Primary Examiner, Art Unit 3688 March 30, 2026
Prosecution Insights