DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This Office action is in response to Applicant’s communication filed on October 27, 2025. Amendments to claims 1 and 10 have been entered.
Claims 1-18 are pending and have been examined. The statement of reasons for the indication of allowable subject matter (over prior art) was already discussed in the Office action mailed on June 25, 2025 and hence not repeated here. The rejections and response to arguments are stated below.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) generating personalized investment recommendations, which is considered a judicial exception because it falls under the category of “Certain Methods of organizing human activity” such as fundamental economic practice as well as commercial or legal interactions including agreements as discussed below. This judicial exception is not integrated into a practical application as discussed below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Analysis
Step 1: In the instant case, exemplary claim 10 is directed to a system (apparatus).
Step 2A – Prong One: The limitations of “A system for generating personalized investment recommendations, the system comprising:
a user device; and
a processor external to and in communication with the user device, the processor is configured to:
receive input from a user through the user device, the input comprising at least one of a text prompt or an audio prompt;
process the input using a first artificial intelligence (AI) model comprising at least one of a recurrent neural network (RNN), deep RNN (DRNN), Q-learning network (QN), or deep Q-learning network (DQN) to generate extracted information;
generate a plurality of responses using generative AIs with the extracted information as input, wherein the generative AIs are iteratively trained using historical data as training input with training parameters adjusted based on performance feedback;
connect the plurality of responses to real-time market data and exclusive datasets to improve quality and relevance of the plurality of responses; and
generate a personalized investment recommendation to the user based on the plurality of responses” as drafted, when considered collectively as an ordered combination without the italicized portions, is a process that, under the broadest reasonable interpretation, covers the category of “Certain Methods of organizing human activity” such as fundamental economic practice as well as commercial or legal interactions including agreements.
Generating personalized investment recommendations is a fundamental economic practice. The steps of “receive input from a user through the user device, the input comprising at least one of a text prompt or an audio prompt; process the input using a first artificial intelligence (AI) model comprising at least one of a recurrent neural network (RNN), deep RNN (DRNN), Q-learning network (QN), or deep Q-learning network (DQN) to generate extracted information; generate a plurality of responses using generative AIs with the extracted information as input, wherein the generative AIs are iteratively trained using historical data as training input with training parameters adjusted based on performance feedback; connect the plurality of responses to real-time market data and exclusive datasets to improve quality and relevance of the plurality of responses; and generate a personalized investment recommendation to the user based on the plurality of responses” considered collectively is a form of fulfilling agreements. Hence, the steps of the claim, considered collectively as an ordered combination without the italicized portions, covers the abstract category of “Certain Methods of organizing human activity”.
That is, other than, a user device, a processor external to and in communication with the user device, a first artificial intelligence (AI) model comprising at least one of a recurrent neural network (RNN), deep RNN (DRNN), Q-learning network (QN), or deep Q-learning network (DQN), generative AIs and exclusive datasets, nothing in the claim precludes the steps from being performed as a method of organizing human activity. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of a user device, a processor external to and in communication with the user device, a first artificial intelligence (AI) model comprising at least one of a recurrent neural network (RNN), deep RNN (DRNN), Q-learning network (QN), or deep Q-learning network (DQN), generative AIs and exclusive datasets to perform all the steps. A plain reading of at least Figures 1 and 4 and associated descriptions in at least paragraphs [0024] – [0027] and [0080] – [0098] reveals that the user device may be a generic devices such as mobile devices, desktop computers etc. The processor may be a generic processor suitably programmed to perform the associated functions. The datasets may be generic datasets suitably programmed to store the associated data/ information. The artificial intelligence (AI) model comprising at least one of a recurrent neural network (RNN), deep RNN (DRNN), Q-learning network (QN), or deep Q-learning network (DQN), and the generative AIs are broadly interpreted to include generic software suitably programmed to perform the associated functions. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements in all the steps are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, claim 10 is directed to an abstract idea.
Step 2B: The claim does 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, using the additional elements (identified above) to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, independent claim 10 is not patent eligible. Independent claim 1 is also not patent eligible based on similar reasoning and rationale.
Dependent claims 2-9, and 11-18, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations only refine the abstract idea further.
For instance, in claims 2 and 11, the steps “wherein the processor is configured to generate the personalized investment recommendation by further performing:
evaluating, using a second AI model, the plurality of responses based on at least one of user preferences, financial goals, or risk tolerance of the user in generating the personalized investment recommendation” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 3 and 12, the steps “wherein the processor is configured to generate the personalized investment recommendation by further performing:
analyzing, by the processor, sentiment information comprising news articles and social media posts to gauge market sentiment and predict potential market trends; and
combining, by the processor, the analyzed sentiment information with the plurality of responses to generate the personalized investment recommendation” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 4 and 13, the steps “wherein the processor is configured to generate the personalized investment recommendation by:
analyzing the plurality of responses in conjunction with tax information of the user to derive a set of tax minimizing responses; and
generating the personalized investment recommendation from the set of tax minimizing responses” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 5 and 14, the steps “further comprising:
ranking, by the processor, a plurality of portfolios in a leaderboard,
wherein the plurality of portfolios comprises a portfolio of the user derived based on the personalized investment recommendation and portfolios of other users” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 6 and 15, the steps “further comprising:
performing, by the processor, virtual portfolio simulations using the plurality of responses for scenario testing” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 7 and 16, the steps “further comprising:
displaying a user prompt to perform a user request in accordance with the personalized investment recommendation on a user device for the user to select” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 8 and 17, the steps “further comprising:
executing, by the processor, the user request in response to a single user input to the user device to select the user prompt,
wherein executing the user request comprises automatically submitting at least one order, in accordance with the personalized investment recommendation without requiring further input from the user” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 9 and 18, the steps “further comprising:
selecting the personalized investment recommendation for execution; and
sharing the personalized investment recommendation with other users to enable review and execution of the selected personalized investment recommendation by the other users” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In all the dependent claims, the judicial exception is not integrated into a practical application because the limitations are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; the claims do not affect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment. In addition, the dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
Response to Arguments
4. The fact that the claims are Patent-Ineligible when considered under the MPEP 2106 has already been addressed in the rejection and hence not all the details of the rejection are repeated here.
Response to Applicants’ arguments regarding Step 2A – Prong one:
The claims recite generating personalized investment recommendations, which is considered a judicial exception because it falls under the category of “Certain Methods of organizing human activity” such as fundamental economic practice as well as commercial or legal interactions including agreements as discussed in the rejection.
Generating personalized investment recommendations is a fundamental economic practice. The steps of “receive input from a user through the user device, the input comprising at least one of a text prompt or an audio prompt; process the input using a first artificial intelligence (AI) model comprising at least one of a recurrent neural network (RNN), deep RNN (DRNN), Q-learning network (QN), or deep Q-learning network (DQN) to generate extracted information; generate a plurality of responses using generative AIs with the extracted information as input, wherein the generative AIs are iteratively trained using historical data as training input with training parameters adjusted based on performance feedback; connect the plurality of responses to real-time market data and exclusive datasets to improve quality and relevance of the plurality of responses; and generate a personalized investment recommendation to the user based on the plurality of responses” considered collectively is a form of fulfilling agreements. Hence, the steps of the claim, considered collectively as an ordered combination without the italicized portions, covers the abstract category of “Certain Methods of organizing human activity”. The additional elements (identified in the claim including the AI models) are broadly interpreted to include generic computer components, used as tools in their ordinary capacity, to apply the abstract idea. The features discussed on page 8 of the remarks such as “connecting...the plurality of responses to real-time market data and exclusive datasets to improve quality and relevance …. the plurality of responses is then connected to real-time market data and exclusive datasets to improve accuracy, quality, and relevance of the plurality of responses …. exclusive datasets such as credit card data, financial analyst opinions, satellite and GPS data, and other alternative data sets may be used to improve the quality of the response” may at best be characterized as an improvement in the abstract idea of generating personalized investment recommendations, using the additional elements as tools in their ordinary capacity, to apply the abstract idea. An improvement in abstract idea is still abstract (SAP America v. Investpic *2-3 (“We may assume that the techniques claimed are “groundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Association for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Hence, Applicants’ arguments are not persuasive.
Response to Applicants’ arguments regarding Step 2A – Prong two:
According to MPEP 2106, limitations that are indicative of integration into a practical application include:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using 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 - see MPEP 2106.05(e).
In the instant case, the judicial exception is not integrated into a practical application, because none of the above criteria is met. Th additional elements in the claims are a user device, a processor external to and in communication with the user device, a first artificial intelligence (AI) model comprising at least one of a recurrent neural network (RNN), deep RNN (DRNN), Q-learning network (QN), or deep Q-learning network (DQN), generative AIs and exclusive datasets to perform all the steps. A plain reading of at least Figures 1 and 4 and associated descriptions in at least paragraphs [0024] – [0027] and [0080] – [0098] reveals that the user device may be a generic devices such as mobile devices, desktop computers etc. The processor may be a generic processor suitably programmed to perform the associated functions. The datasets may be generic datasets suitably programmed to store the associated data/ information. The artificial intelligence (AI) model comprising at least one of a recurrent neural network (RNN), deep RNN (DRNN), Q-learning network (QN), or deep Q-learning network (DQN), and the generative AIs are broadly interpreted to include generic software suitably programmed to perform the associated functions. Similarly, the generative AI module utilizing any one or combination of a variety of different models in generating strategies/responses, including but not limited to generative adversarial networks (GANs), variational auto-encoders (VAEs), auto- regressive models, transformers are broadly interpreted to include generic software suitably programmed to perform the associated functions. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements in all the steps are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, the claims are directed to an abstract idea.
The Applicants have not shown how the use of the generative AI module utilizing any one or combination of a variety of different models in generating strategies/responses has resulted in an improvement in the technology of the generative AI module. Instead, these modules are used, as tools in their ordinary capacity, to apply the abstract idea of generating personalized investment recommendations. It does not involve any improvements to another technology, technical field, or improvements to the functioning of the computer itself. Hence, Applicants’ arguments are not persuasive.
Response to Applicants’ arguments regarding Step 2B:
As discussed in the rejection, 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, using the additional elements (identified in the rejection) to perform the claimed steps, amount to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claims are not patent eligible.
Using the generative AIs to produce new contents or responses with textual inputs using neural network techniques such as, but not limited to, auto-regressive models, transformers, Generative Adversarial Networks (GANs), Variational, Autoencoders (VAEs), etc. does not imply that the generative AI technology has improved. There is a fundamental difference
between computer functionality improvements, on the one hand, and uses of existing computers as tools to perform a particular task, on the other. There is nothing, for example, in the pending claims to suggest that the claimed “generative AI” is somehow made more efficient or that the manner in which the AI carries out its basic functions is otherwise improved in any way. The alleged advantages that Applicants tout do not concern an improvement in computer capabilities but instead relate to an alleged improvement in the decisions relating to “receiving, processing, generating, connecting and generating” in the context of generating personalized investment recommendations, for which a computer system is used as a tool in its ordinary capacity. The computer system is merely a platform on which the abstract idea is implemented. Hence, the claims do not recite significantly more than an abstract idea. Therefore, the Applicants’ arguments are not persuasive.
For these reasons and those discussed in the rejection, the rejections under 35 USC § 101 are maintained.
Applicant’s other arguments with respect to pending claims have been considered but are not persuasive.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(a) Bjontegard; Bernt Erik (US Pub. 20250322460 A1) discloses a system for managing financial portfolio as well as for recommending personalized investment strategies, is disclosed. The system includes a first computing unit having an application interface, communicably connected to a central controller. The central controller includes a back-end server. The backend server includes a data receiving component adapted to receive the input data-sets from the first computing unit and real time data-sets from a plurality of data-sources. The backend server further includes a data analysis module adapted to process the real-time data to generate one or more actionable insights. The backend server furthermore includes a contextually intelligent portfolio management module adapted to utilize one or more contextual data related to the user, to monitors the user's investments and asset portfolios, and generate contextually relevant portfolio information for the user in a real-time. The backend server additionally includes a financial strategy implementation module adapted to utilize the actionable insights in combination with the contextually relevant portfolio information of the user to generate personalized investment strategies and recommendations for each user. In operation, a user generates and/or formulate at least one input query based at least in part on one or more input data-sets related to the user's financial portfolio. Thereafter, the input datasets are received at the back-end server which in turn are processed by the financial strategy implementation module in combination with one or more actionable insights generated by the data analysis module of the back-end server to identify a response to the input query, and/or provide one or more personalized investment related recommendation. The identified information and/or recommendation is elicited as a response to the input query and is presented and/or visualized on an output component of the first computing unit.
6. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Narayanswamy Subramanian whose telephone number is (571) 272-6751. The examiner can normally be reached Monday-Friday from 9:00 AM to 5:00 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Abhishek Vyas can be reached at (571) 270-1836. The fax number for Formal or Official faxes and Draft to the Patent Office is (571) 273-8300.
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/Narayanswamy Subramanian/
Primary Examiner
Art Unit 3691
February 13, 2026