DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendment filed 11/12/2025.
Claims 1, 8, and 15 have been amended. Claims 1-2, 4-9, 11-16, and 18-21 are pending and have been examined on the merits (claims 1, 8, and 15 being independent).
The amendment filed 11/12/2025 to the claims has been entered.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09/15/2025 and 12/10/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments and amendments filed 11/12/2025 have been fully considered.
Applicants assert that the pending claims fully comply with the requirement of 35 U.S.C. 101. Examiner respectfully disagrees. Applicant’s argument and amendments have been considered and are not persuasive. The rejections under 35 U.S.C. 101 have been maintained and clarified in view of the USPTO MPEP 2106.
Applicant arguments (see Applicant’s remarks, pages 8-13):
(1) Applicant’s arguments that “Applicant respectfully submits that amended claim 1 recites a specific RLHF training process with reward model training that solves technical problems in conversational AI systems. Specifically, amended claim 1 addresses the technical problem of aligning AI systems with target response characteristics through computational feedback.” (see remarks, page 9), are not found persuasive.
Examiner response: In the present application, Examiner considers that the instant claims do not integrate the judicial exception into a practical application because additional elements such as “wherein the ML model is trained at least in part using reinforcement learning with human feedback (RLHF) including training a reward model by ……” in recited amendment in claim 1 does not apply, rely on, or use the judicial exception in a manner that that imposes a meaningful limitation on the judicial exception (i.e. apply it with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)). So the instant recited claims including additional elements do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking/applying the use of an abstract idea to a particular technological environment (i.e. machine learning (ML) chatbot or reinforcement learning with human feedback (RLHF)). That is, the “machine learning (ML) chatbot” merely uses a memory and/or a storage (e.g., a database) as an inherited function in order to store information in certain period and “reinforcement learning with human feedback (RLHF)” a technique in machine learning that aims to align an intelligent agent's behavior with human preferences. This is achieved by training a reward model based on human feedback, which is then used to guide the agent's actions through reinforcement learning. The training process of RLHF involves some steps as training a reward model to receive model responses and to output scalar rewards as processing inherited functions. Thus, Applicant’s arguments are not persuasive.
(2) Applicant’s arguments that “The claimed elements work together to improve computer functionality. The RLHF training process with reward model training based upon rankings, combined with the dual memory architecture for maintaining conversational state, creates a specific ML system that addresses technical challenges in conversational AL.” (see remarks, page 10), are not found persuasive.
Examiner response: As set forth in above, the instant recited claims including additional elements do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking/applying the use of an abstract idea to a particular technological environment (i.e. machine learning (ML) chatbot or reinforcement learning with human feedback (RLHF)). That is, the “machine learning (ML) chatbot” merely uses a memory and/or a storage (e.g., a database) as an inherited function in order to store information in certain period and “reinforcement learning with human feedback (RLHF)” a technique in machine learning that aims to align an intelligent agent's behavior with human preferences. This is achieved by training a reward model based on human feedback, which is then used to guide the agent's actions through reinforcement learning. The training process of RLHF involves some steps as training a reward model to receive model responses and to output scalar rewards as processing inherited functions. Thus, Applicant’s arguments are not persuasive.
(3) Applicant’s arguments that “Therefore, the present Application sets forth additional improvements: (i) dividing objectives into discrete steps enables systematic processing of complex investment goals; and (ii) the RLHF training process with reward model training based upon rankings enables iterative refinement of ML model outputs, resulting in increased efficiency over time.” (see remarks, page 12), are not found persuasive.
Examiner response: As achieving the investment goals using a particular technological environment such as machine learning (ML) chatbot or reinforcement learning with human feedback (RLHF)) with processing inherited functions, it does not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Thus, Applicant’s arguments are not persuasive.
(4) Applicant’s arguments that “The recent USPTO precedential decision confirms that specific ML training methodologies are patent-eligible. In Ex parte Desjardins, the USPTO Appeals Review Panel addressed claims directed to training machine learning models with specific parameter importance measures and posterior distribution approximations. The decision recognized that claims reciting specific ML training processes with concrete computational steps constitute patent-eligible subject matter.” (see remarks, page 13), are not found persuasive.
Examiner response: The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea over merely using “machine learning (ML) chatbot” and “reinforcement learning with human feedback (RLHF)” as processing inherited functions. The claims are directed towards an abstract idea for how to achieve the investment goals and merely uses the computer (including machine learning (ML) chatbot or reinforcement learning with human feedback (RLHF))) as a tool to do so. The use of machine learning (ML) chatbot or reinforcement learning with human feedback (RLHF)) to choose which experience set is to be associated with the transaction is akin analyzing information as training a model. Further, machine learning (ML) chatbot or reinforcement learning with human feedback (RLHF)) are recite at a high level of generality such that is amounts to applying the abstract idea to the computing environment, and therefore under broadest reasonable interpretation, the processing inherited functions of “machine learning (ML) chatbot” and “reinforcement learning with human feedback (RLHF) are akin to performing a series of mathematical calculations. Thus, Applicant’s arguments are not persuasive.
With regard to the rejections of claims 1-2, 4-9, 11-16, and 18-21 under 35 U.S.C. 103, Applicant’s arguments and amendments have been considered but are moot as a new ground of rejection has been added and Examiner respectfully disagrees. Examiner notes that Applicant is arguing newly amended claim language. As noted in the citation above the prior art and it is addressed by the rejections under 35 USC 103.
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 1-2, 4-9, 11-16, and 18-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, (1) 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, (2a) 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 (2b), 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 significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. (2014).
The claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In the instant case, the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
Step (1): In the instant case, the claims are directed towards to a method for providing an investment instruction (e.g., a recommendation) for a financial goal to an individual which contains the steps of receiving, sending, receiving, and communicating. The claim recites a series of steps and, therefore, is a process. The claims do fall within at least one of the four categories of patent eligible subject matter because claim 1 is direct to a computer system, claim 8 is direct to a method, and claim 15 is direct to a computer readable storage medium, i.e. machines programmed to carrying out process steps, Step 1-yes.
Step (2A) Prong 1: A method for providing an investment instruction (e.g., a recommendation) for a financial goal to an individual is akin to the abstract idea subject matter grouping of: Certain Methods of Organizing Human Activity as fundamental economic principles or practices and commercial or legal interactions. As such, the claims include an abstract idea.
The specific limitations of the invention are (a) identified to encompass the abstract idea include: receiving… an investment strategy…, sending… the investment strategy…, receiving… investment instructions…, and communicating… investment instructions to the investor.
As stated above, this abstract idea falls into the (b) subject matter grouping of: Certain Methods of Organizing Human Activity as fundamental economic principles or practices and commercial or legal interactions.
Step (2A) Prong 2: The instant claims do not integrate the exception into a practical application because additional elements: 1) “long-term memory on a database of a server” and “short-term memory on a memory of the server” amount to simply applying the abstract idea to a computer component. (e.g. “apply it”) 2) “a machine learning (ML) chatbot”, “an ML model”, and “reinforcement learning with human feedback (RLHF) do not apply, rely on, or use the judicial exception in a manner that that imposes a meaningful limitation on the judicial exception (i.e. generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h) or apply it with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
The instant recited claims including additional elements (i.e., processors, a machine learning (ML) chatbot, an ML model, reinforcement learning with human feedback (RLHF), long-term memory on a database of a server, short-term memory on a memory of the server) do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The limitations merely use a generic computing technology (Specification paragraph [0007]: local or remote processors, servers, transceivers, sensors, memory units, mobile devices, wearables, smart watches, voice bots or chatbots, ChatGPT bots, wired or wireless communication, ML chatbot (or voice bot), an ML model, etc.) as generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h) or apply it with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)). Therefore, the claims are directed to an abstract idea.
Step (2B): The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (Claims: e.g., processors, a machine learning (ML) chatbot, an ML model, reinforcement learning with human feedback (RLHF), long-term memory on a database of a server, short-term memory on a memory of the server) amount to no more than mere instructions to apply the exactly using generic computer component. The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea over a generic computer network with a generic computer element. The computer is merely a platform on which the abstract idea is implemented. Simply executing an abstract concept on a computer does not render a computer “specialized,” nor does it transform a patent-ineligible claim into a patent-eligible one. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1280 (Fed. Cir. 2012). There are no improvements to another technology or technical field, no improvements to the functioning of the computer itself, transformation or reduction of a particular article to a different state or thing or any other meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment as a result of performing the claimed method. Hence, the claims do not recite significantly more than an abstract idea. In conclusion, merely “linking/applying” the exception using generic computer components does not constitute ‘significantly more’ than the abstract idea. (MPEP 2106.05 (f)(h)). Therefore, the claims are not patent eligible under 35 USC 101.
Dependent claims 2, 4-7, 9, 11-14, 16, and 18-21 when analyzed as a whole and in an ordered combination are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as detailed below. The additional recited limitations in the dependent claims only refine the abstract idea.
For instance, in claims 2, 9, and 16, the step of “… wherein the investment strategy comprises a risk tolerance of the investor, an identification of one or more publicly traded companies, an identification of one or more financial assets, an identification of one or more classes of financial assets,…..” (i.e., obtaining investment data), in claims 4, 11, and 18, the step of “… train the ML model with a training dataset, and validate the ML model with a validation dataset, wherein the training dataset and the validation dataset comprise a set of historical financial transactions, a set of historical information regarding one or more financial markets,...” (i.e., training ML model), in claims 5, 12, and 19, the step of “… send market prices of financial assets and a prompt for updated investment instructions to the ML chatbot to cause the ML model to generate the updated investment instructions, receive the updated investment instructions from the ML chatbot, ...” (i.e., updating investment data), in claims 6 and 13, the step of “… send an identification of current assets of the investor and a prompt for updated investment instructions to the ML chatbot to cause the ML model to generate the updated investment instructions, receive the updated investment instructions from the ML chatbot, and communicate the updated investment instructions ...” (i.e., updating investment data), in claims 7, 14, and 20, the step of “… perform one or more financial transactions according to the investment instructions...” (i.e., performing financial transactions), and in claim 21, the step of “… train the ML model with training data indicating associations between investment and investment instructions to accomplish the strategies.” (i.e., training ML model) are all processes that, under its broadest reasonable interpretation, covers performance of a fundamental economic practice but for the recitation of a generic computer component. Performing a financial transaction based on a financial goal using ML model is a most fundamental commercial process.
This is an abstract concept with nothing more and is also considered mere instructions to apply an exception akin to a commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd.; Gottschalk and Versata Dev. Group, Inc.; see MPEP 2106.05(f)(2).
In dependent claims 2, 4-7, 9, 11-14, 16, and 18-21, the step claimed are rejected under the same analysis and rationale as the independent claims 1, 8, and 15 above. Merely claiming the same process using ML model to meet a financial goal based on a recommendation does not change the abstract idea without an inventive concept or significantly more. Clearly, the additional recited limitations in the dependent claims only refine the abstract idea further. Further refinement of an abstract idea does not convert an abstract idea into something concrete.
Therefore, claims 1-2, 4-9, 11-16, and 18-21 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
In the rejections below, where claims are currently amended, this is indicated by underlining.
Claims 1-2, 4, 7-9, 11, 14-16, 18, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Kalluri, US Patent Number US 11,444,893 B1 in view of Liu Hengjiang (hereinafter Liu), CN 114742657 A in view of Edwards et al. (hereinafter Edwards), US Publication Number 2020/0372823 A1 in further view of Long Ouyang et al. (hereinafter Long Ouyang), NPL (Title: Training language models to follow instructions with human feedback, Author: Long Ouyang et al., Source: Cornell University, Date: March 4, 2022)
Regarding claim 1:
Kalluri discloses the following:
A computer system for generating personalized investment instructions using a machine learning (ML) chatbot with context-responsive memory for maintaining conversational state across user interactions, the computer system comprising: (Kalluri: See column 10, lines 4-31: “"machine learning models" input 320 may include a classifier that is trained by applying supervised learning to words spoken during past conversations with a chatbot. One or more outputs of provider computing system 110 may include, but are not limited to, "recommended products and services" output 325, "maturity metric" output 330, and "confidence score" output 335. In some embodiments, "recommended products and services" output 325 includes a generated recommendation to offer to a user based on the determined maturity metric of the user. For example, if the determined maturity metric of the user is estimated to be an age range of about 15 to 25 years of age, the "recommended products and services" output 325 may include a college savings plan or student credit card option. A recommendation may be for a type of retirement plan, a savings plan, credit card plans, investment options, payment plans for loans or mortgages, etc.”)
one or more processors; and a memory storing executable instructions thereon that, when executed by the one or more processors, cause the one or more processors to: (Kalluri: See column 19, lines 4-18: “the one or more processors may execute instructions stored in the memory or may execute instructions otherwise accessible to the one or more processors.”)
receive the investment instructions from the ML chatbot, and (Kalluri: See column 10, lines 4-31: “"machine learning models" input 320 may include a classifier that is trained by applying supervised learning to words spoken during past conversations with a chatbot…. if the determined maturity metric of the user is estimated to be an age range of about 15 to 25 years of age, the "recommended products and services" output 325 may include a college savings plan or student credit card option. A recommendation may be for a type of retirement plan, a savings plan, credit card plans, investment options, payment plans for loans or mortgages, etc.”)
wherein the ML chatbot is configured to store: (i) investor information in long-term memory on a database of a server, and (ii) current state information in short-term memory on a memory of the server. (Kalluri: See column 6, lines 7-13: “chatbot manager 245 may be configured to instruct a chatbot to engage in a conversation with a customer. Such a conversation may be conducted by, for example, capturing a customer's spoken words (or other communications), analyzing the communication to better understand context and identify user needs, and responding to the customer”; column9, lines 14-40: “"Phrases from ongoing conversation" input 305 may include words and 15 phrases of a current interaction between a customer via a user device 115 of the customer and the provider computing system 110 (e.g., a chatbot, a user device 115 of an advisor, etc.). In some embodiments, the "phrases from ongoing conversation" input 305 may be received in real-time via the network interface 215 or near real-time ( e.g., as new, written messages are received from the user device 115 of the customer or audio data from words spoken by the customer that are detected by a microphone of the user device 115). The "phrases from ongoing conversation" input 305 may be utilized by the machine learning engine 260 in determining a maturity metric of the current user interacting with a chatbot.”; column13, lines 38-52: “long short term memory (LSTM) recurrent neural networks (RNNs), gradient boosted trees, logistic regression, hidden and basic Markov models, and frequent pattern growth algorithms may be utilized in classifying patterns and decisions while training the machine learning engine 260.”, and see also column 5, lines 13-47)
Kalluri does not explicitly disclose the following, however Liu further teaches:
receive, from an investor, an investment strategy, wherein the investment strategy comprises an objective and a monetary goal, and wherein the monetary goal includes staying above a specified balance (reads on "loss does not exceed 10%"), maintaining a specified income from dividends or interest (reads on “profit 1000 yuan”), and/or reaching a specified rate of return (reads on “the investment goals of the user may be 10% annual return”), (Liu: See page 8, lines 25-34, and notes: Examiner considers the claim only requires to teach at least one from multiple elements listed above.)
communicate the investment instructions to the investor. (Liu: See page 7, lines 12-14: “the user (e.g., investor, etc.) may be the owner of the user terminal 130. In some embodiments, user terminal 130 may be used for interaction and display with a user. For example, the user terminal 130 may obtain the investment goal and the risk goal of the user through user input.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the use of machine learning or deep learning techniques to train models for determining maturity metrics of unknown users conversing with a chatbot based on of Kalluri to include the monetary goals such as staying above a specified balance, maintaining a specified income from dividends or interest, or reaching a specified rate of return, as taught by Liu, in order to provide more options for future goals. (Liu, see pages 7-8)
Kalluri and Liu do not explicitly disclose the following, however Edwards further teaches:
send (reads on “machine learning module 120 may receive, as input, one or more streams of user activity. The one or more streams of user activity may correspond to actions taken by the user with respect to the user's accounts with organization computing system 104. Such streams of activity may include historical transaction data of the user, as well as, accounts payment, navigation of web pages, calls to customer service, chat sessions with a bot”) the investment strategy and a prompt for investment instructions to the ML chatbot to cause an ML model to generate the investment instructions, wherein the objective is divided into a plurality of discrete steps, and wherein the ML model is trained (reads on “machine learning module 120 may use one or more of a decision tree learning model, association rule learning model, artificial neural network model, deep learning model, inductive logic programming model, support vector machine model, clustering model, Bayesian network model, reinforcement learning model”) at least in part using reinforcement learning with human feedback (RLHF), (Edwards: See paragraph [0047], and notes: Examiner considers that “reinforcement learning with human feedback (RLHF)” reads on “reinforcement learning model” as cited above.)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the use of machine learning or deep learning techniques to train models for determining maturity metrics of unknown users conversing with a chatbot based on of Kalluri to include machine learning module that uses one or more of a decision tree learning model, association rule learning model, artificial neural network model, deep learning model, inductive logic programming model, support vector machine model, clustering model, Bayesian network model, reinforcement learning model, as taught by Edwards, in order to generate more suitable responses for future goals. (Edwards, see [0047])
Kalluri, Liu, and Edwards do not explicitly disclose the following, however Long Ouyang further teaches:
including training a reward model (reads on “train a reward model”) by (i) receiving rankings for model responses to a same prompt (reads on “our RM dataset, with labeler rankings of model outputs used to train our RMs”), and (ii) training the reward model to output scalar rewards (reads on “we trained a model to take in a prompt and response, and output a scalar reward”) representing outcomes based upon the rankings, (see Long Ouyang, abstract, and pages 2: “We focus on fine-tuning approaches to aligning language models. Specifically, we use reinforcement learning from human feedback (RLHF; Christiano et al., 2017; Stiennon et al., 2020) to fine-tune GPT-3 to follow a broad class of written instructions (see Figure 2).”, page 6: “Step 2: Collect comparison data, and train a reward model. We collect a dataset of comparisons between model outputs, where labelers indicate which output they prefer for a given input. We then train a reward model to predict the human-preferred output. Step 3: Optimize a policy against the reward model using PPO. We use the output of the RM as a scalar reward. We fine-tune the supervised policy to optimize this reward using the PPO algorithm (Schulman et al., 2017).”, page 7: “From these prompts, we produce three different datasets used in our fine-tuning procedure: (1) our SFT dataset, with labeler demonstrations used to train our SFT models, (2) our RM dataset, with labeler rankings of model outputs used to train our RMs”, and page 8: “Reward modeling (RM). Starting from the SFT model with the final unembedding layer removed, we trained a model to take in a prompt and response, and output a scalar reward.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the use of machine learning or deep learning techniques to train models for determining maturity metrics of unknown users conversing with a chatbot based on of Kalluri to include training a reward model to predict the human-preferred output as a scalar reward using reinforcement learning from human feedback, as taught by Edwards, in order to generate a more suitable output for future goals. (see Long Ouyang, abstract and pages 2, 6, 7, and 8)
Regarding claim 2:
Kalluri and Liu do not explicitly disclose the following, however Edwards further teaches:
The computer system of claim 1, wherein the investment strategy comprises a risk tolerance of the investor, an identification of one or more publicly traded companies, an identification of one or more financial assets, an identification of one or more classes of financial assets, an identification of current investments of the investor, a monetary goal (reads on “a monetary amount associated with the financial goal”), and/or a deadline to achieve (reads on “an estimated timeline for when the user will reach the monetary amount.”) the monetary goal. (Edwards: See paragraph [0042] “The target savings amount may be determined by goal manager 118 using a machine learning model generated by machine learning module 120. For example, goal manager 118 may generate the target savings amount for the financial goal based on one or more spending habits of the user. Goal manager 118 may parse through the transaction history of the user to identify how the user manages his or her money to determine both a monetary amount associated with the financial goal and an estimated timeline for when the user will reach the monetary amount.”, and Notes: the recited limitation does not require to teach all elements of the investment strategy that comprises a risk tolerance of the investor, an identification of one or more publicly traded companies,…..)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the use of machine learning or deep learning techniques to train models for determining maturity metrics of unknown users conversing with a chatbot based on of Kalluri to include machine learning module that uses one or more of a decision tree learning model, association rule learning model, artificial neural network model, deep learning model, inductive logic programming model, support vector machine model, clustering model, Bayesian network model, reinforcement learning model, as taught by Edwards, in order to generate more suitable responses for future goals. (Edwards, see [0047])
Regarding claim 4:
Kalluri and Liu do not explicitly disclose the following, however Edwards further teaches:
The computer system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
train the ML model with a training dataset, and (Edwards: See paragraph [0047] “Machine learning module 120 may include one or more computer systems configured to train a prediction model used by goal manager 118. To train the prediction model, machine learning module 120 may receive, as input, one or more streams of user activity.”, and see also [0054])
validate the ML model with a validation dataset, wherein the training dataset and the validation dataset comprise a set of historical financial transactions, a set of historical information regarding one or more financial markets, and/or a set of public financial data about one or more assets. (Edwards: See paragraph [0047] “Machine learning module 120 may include one or more computer systems configured to train a prediction model used by goal manager 118. To train the prediction model, machine learning module 120 may receive, as input, one or more streams of user activity. The one or more streams of user activity may correspond to actions taken by the user with respect to the user's accounts with organization computing system 104. Such streams of activity may include historical transaction data of the user, as well as, accounts payment, navigation of web pages, calls to customer service, chat sessions with a bot, interactions with emails from organization computing system 104, and the like.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the use of machine learning or deep learning techniques to train models for determining maturity metrics of unknown users conversing with a chatbot based on of Kalluri to include machine learning module that uses one or more of a decision tree learning model, association rule learning model, artificial neural network model, deep learning model, inductive logic programming model, support vector machine model, clustering model, Bayesian network model, reinforcement learning model, as taught by Edwards, in order to generate more suitable responses for future goals. (Edwards, see [0047])
Regarding claim 7:
Kalluri and Liu do not explicitly disclose the following, however Edwards further teaches:
The computer system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
perform one or more financial transactions according to the investment instructions. (Edwards: See figs. 5A and 5B, paragraph [0082] “Second graphical element 506 may be representative of a progress bar associated with a financial goal. For example, gamification element 5031 may include second graphical element 5061 corresponding to a progress bar for that illustrates a current savings amount of $350; gamification element 5032 may include second graphical element 5062 corresponding to a progress bar that illustrates a current savings amount of $0; and gamification element 5033 may include a third graphical element 5063 corresponding to a progress bar that illustrates a current savings amount of $770.”, and see [0083])
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the use of machine learning or deep learning techniques to train models for determining maturity metrics of unknown users conversing with a chatbot based on of Kalluri to include machine learning module that uses one or more of a decision tree learning model, association rule learning model, artificial neural network model, deep learning model, inductive logic programming model, support vector machine model, clustering model, Bayesian network model, reinforcement learning model, as taught by Edwards, in order to generate more suitable responses for future goals. (Edwards, see [0047])
Regarding claim 21:
Kalluri and Liu do not explicitly disclose the following, however Edwards further teaches:
The computer-readable storage medium of claim 15, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: train the ML model with training data indicating associations between investment and investment instructions to accomplish the strategies. (Edwards: See paragraph [0054] “organization computing system 104 may leverage a machine learning algorithm, trained by machine learning module 120, to identify a financial goal of the user by providing, as input to the machine learning algorithm, one or more transactions (e.g., transactions 128) of the user. Accordingly, goal manager 118 may prompt the user with one or more potential financial goals based on a spending history of the user.”, and see also [0047])
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the use of machine learning or deep learning techniques to train models for determining maturity metrics of unknown users conversing with a chatbot based on of Kalluri to include machine learning module that uses one or more of a decision tree learning model, association rule learning model, artificial neural network model, deep learning model, inductive logic programming model, support vector machine model, clustering model, Bayesian network model, reinforcement learning model, as taught by Edwards, in order to generate more suitable responses for future goals. (Edwards, see [0047])
Regarding claims 8 and 15: it is similar scope to claim 1, and thus it is rejected under similar rationale.
Regarding claims 9 and 16: it is similar scope to claim 2, and thus it is rejected under similar rationale.
Regarding claims 11 and 18: it is similar scope to claim 4, and thus it is rejected under similar rationale.
Regarding claims 14 and 20: it is similar scope to claim 7, and thus it is rejected under similar rationale.
Claims 5-6, 12-13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kalluri in view of Liu in view of Edwards in view of Long Ouyang in further view of Wu et al. (hereinafter Wu), US Publication Number 2024/0144372 A1.
Regarding claim 5:
Kalluri, Liu, Edwards, and Long Ouyang do not explicitly disclose the following, however Wu further teaches:
The computer system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
send market prices of financial assets and a prompt for updated investment instructions to the ML chatbot to cause the ML model to generate the updated investment instructions, (Wu: See paragraph [0021] “the neural network(s) may be trained using training data and corresponding ground truth data. The training data may include, but is not limited to, user data, news data (e.g., representing past news associated with investments), financial data (e.g., representing past prices associated with investments), prediction data (e.g., representing past price predictions associated with investments), and/or any other type of data. Additionally, the ground truth data may represent actual prices of investments, actual price movements of investments, actual investments acquired/held/sold/traded by users, and/or the like.”, and see also [0025])
receive the updated investment instructions from the ML chatbot, and (Wu: See paragraph [0022] “Based on identifying the intent, the dialogue system may perform one or more of the processes described herein to determine a financial prediction associated with the user speech. For example, if the user speech includes "What will the price of Investment X be next week," then the financial prediction may include the predicted movement of the investment and/or the future predicted price of the investment.”, and see also [0021])
communicate the updated investment instructions to the investor. (Wu: See paragraph [0022] “The dialogue system may then provide the financial prediction to the user, such as in the form of audio data representing one or more words describing the financial prediction.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the use of machine learning or deep learning techniques to train models for determining maturity metrics of unknown users conversing with a chatbot based on of Kalluri to include using neural networks to determine financial investment predictions or recommendations, as taught by Wu, in order to generate more suitable responses for the financial recommendations. (Wu, see paragraphs [0001-0002])
Regarding claim 6:
Kalluri, Liu, Edwards, and Long Ouyang do not explicitly disclose the following, however Wu further teaches:
The computer system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
send an identification of current assets of the investor and a prompt for updated investment instructions to the ML chatbot to cause the ML model to generate the updated investment instructions, (Wu: See paragraph [0023] “the system(s) may store a user profile associated with the user, where the user profile includes one or more identifiers of one or more investments that the user has bought/sold/traded/acquired/showed interest in.”, and see also [0025])
receive the updated investment instructions from the ML chatbot, and (Wu: See paragraph [0023] “the system(s) may perform one or more of the processes described herein to automatically determine one or more financial predictions associated with the acquired investment(s).”)
communicate the updated investment instructions to the investor. (Wu: See paragraph [0023] “The system(s) may then provide the financial prediction(s) to the user, such as via a user device.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the use of machine learning or deep learning techniques to train models for determining maturity metrics of unknown users conversing with a chatbot based on of Kalluri to include using neural networks to determine financial investment predictions or recommendations, as taught by Wu, in order to generate more suitable responses for the financial recommendations. (Wu, see paragraphs [0001-0002])
Regarding claims 12 and 19: it is similar scope to claim 5, and thus it is rejected under similar rationale
Regarding claim 13: it is similar scope to claim 6, and thus it is rejected under similar rational
Conclusion
The prior art made of record but not relied upon herein but pertinent to Applicant’s disclosure is listed in the enclosed PTO-892.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YONG S PARK whose telephone number is (571)272-8349. The examiner can normally be reached on M-F 9:00-5:00 PM, EST.
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/YONGSIK PARK/Examiner, Art Unit 3694
January 22, 2026
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694