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 .
This application has PRO 63/463,661 05/03/2023
This application has PRO 63/461,065 04/21/2023
Claim Status
Claim 1-7, 9-11, 13-16, and 18-23 are currently pending and rejected.
Claim 8, 12 and 17 are cancelled.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, they recite the same limitations with slightly different wordings.
Claim 2 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 3 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 4 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 5 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 5 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 6 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 6 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 7 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 7 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 10 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 10 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, they recite the same limitations with slightly different wordings.
Claim 11 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 11 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, they recite the same limitations with slightly different wordings.
Claim 13 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 14 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 10 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 15 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 10 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, they recite the same limitations with slightly different wordings.
Claim 16 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 11 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, they recite the same limitations with slightly different wordings.
Claim 18 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 19 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 10 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 21 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 11 of copending Application No. 18/638,514 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejection – 35 U.S.C. 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, 9-11, 13-16, and 18-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The rationale for this finding is explained below. In the instant case, the claims are directed towards investment portfolio rebalancing. The concept is clearly related to a longstanding economic practice and managing human trading behavior, thus the present claims fall within the Certain Method of Organizing Human Activity grouping. Examiner also points out that the claim language does not provide any detail with regards to the AI models. Under the broadest reasonable interpretation, the AI models are merely automating human processes. Therefore, the present claims also fall within the Mental Processes grouping. The claims do not include limitations that are “significantly more” than the abstract idea because the claims do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Note that the limitations, in the instant claims, are done by the generically recited computer device. The limitations are merely instructions to implement the abstract idea on a computer and require no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry. Therefore, claims 1-7, 9-11, 13-16, and 18-23 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Step 1: The claims 1-7, 9-11, 13-16, and 18-23 are directed to a process, machine, manufacture, or composition matter.
In Alice Corp. Pty. Ltd. v. CLS Bank Intern., 134 S. Ct. 2347 (2014), the Supreme Court applied a two-step test for determining whether a claim recites patentable subject matter. First, we determine whether the claims at issue are directed to one or more patent-ineligible concepts, i.e., laws of nature, natural phenomenon, and abstract ideas. Id. at 2355 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1296–96 (2012)). If so, we then consider whether the elements of each claim, both individually and as an ordered combination, transform the nature of the claim into a patent-eligible application to ensure that the patent in practice amounts to significantly more than a patent upon the ineligible concept itself.
Claims 1-11, 13-14, and 20-23 are directed to a method (i.e., a process), and claims 15, 16, and 18-19 are directed to a system (i.e., a machine).
Step 2A: The claims are directed to an abstract idea.
Prong One
The present claims are directed towards investment portfolio rebalancing. The concept comprises receiving data associated with a user and market condition information, generating a plurality of investment recommendations using a first AI model, ranking the plurality of investment recommendations using a second AI model, generating and presenting to the user the finalized investment recommendations, rebalancing the user’s portfolio in response to user selection of finalized investment recommendations. The concept is clearly related to a longstanding economic practice of portfolio management and managing human trading behavior, thus the present claims fall within the Certain Method of Organizing Human Activity grouping. Examiner also points out that the claim language does not provide any detail with regards to the AI models to distinguish their processes from human mental processes. Under the broadest reasonable interpretation, the AI models are merely automating human processes. Therefore, the present claims also fall within the Mental Processes grouping. The performance of the claim limitations using generic computer components (i.e., a processor) does not preclude the claim limitation from being in the certain methods of organizing human activity grouping or mental processes grouping. The use of artificial intelligence to generate investment recommendations also does not render the claims less abstract, because AI models are merely performing calculations and making decisions analogous to human processes. The claimed invention does not improve AI technology itself. Accordingly, this claim recites an abstract idea.
Prong Two
Independent claim 1 and 10 recite a processor as additional element. Dependent claims 2-9, 11, 13-14, and 20-23 do not recite any other additional element. Independent claim 15 recites a database and a processor as additional elements. Dependent claims 16 and 18-19 do not recite any other additional element. The additional elements are claimed to perform basic computer functions, such as receiving data (note: insignificant extra-solution), generating and ranking investment recommendations (note: insignificant extra-solution), presenting finalized investment recommendations to the user (note: insignificant extra-solution), and rebalancing an investment portfolio in response to user selection (note: insignificant extra-solution). The use of artificial intelligence to generate and rank investment recommendations also does not render the claims less abstract, because existing AI models are used to perform calculations and make decisions analogous to human processes in investment advisory environment. The claimed invention does not improve AI technology itself. The recitation of the computer elements amounts to mere instruction to implement an abstract concept on computers. Clearly, the claimed invention merely applies existing AI technology in portfolio balancing environment. In the Recentive Analytics v. Fox Corp case, the Federal Circuit ruled patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible. The recitation of the computer elements amounts to mere instruction to implement an abstract concept on computers. The present claims do not solve a problem specifically arising in the realm of computer networks. Rather, the present claims implement an abstract concept using existing AI technology in a networked computer environment. The present claims do not recite limitation that improve the functioning of computer, effect a physical transformation, or apply the abstract concept in some other meaningful way beyond generally linking the use of the abstract concept to a particular technological environment. As such, the present claims fail to integrate into a practical application.
Step 2B: The claims do not recite additional elements that amount to significantly more than the abstract idea.
As discussed earlier, independent claim 1 and 10 recite a processor as additional element. Dependent claims 2-9, 11, 13-14, and 20-23 do not recite any other additional element. Independent claim 15 recites a database and a processor as additional elements. Dependent claims 16 and 18-19 do not recite any other additional element. The additional elements are claimed to perform basic computer functions, such as receiving data, generating and ranking investment recommendations (i.e., performing calculations), presenting finalized investment recommendations to the user (i.e., displaying result of analysis), and rebalancing an investment portfolio in response to user selection (i.e., transmitting order instructions via network). According to MPEP 2106.05(d), “performing repetitive calculations”, “receiving, processing, and storing data”, “electronically scanning or extracting data from a physical document”, “electronic recordkeeping”, “storing and retrieving information in memory”, and “receiving or transmitting data over a network, e.g., using the Internet to gather data” are considered well-understood, routine, and conventional functions of computer. The use of AI technology to generate recommendations is also not inventive. The present claims do not improve the functioning of computer or AI technology.
New Claim 20 recites the first AI model can be neural network including a recurrent neural network, a deep recurrent neural network, a long short-term memory network, a convolutional neural network, a fully connected neural network or a large multimodal language model configured to process different types of input data. The claim language is unspecific and lists well-known neural networks in the field. Similarly, new claim 21 recites the second AI model can be generative adversarial network, a variational auto-encoder, an auto-regressive model, or a transfer. The claim language is unspecific and lists well-known models in the field. Claim 22 recites AI models are iteratively trained using historical data as training input, but this is how most AI models are trained. Claim 23 recites input-layer, hidden-layer, and output-layer, but this arrangement is the same as conventional AI models. The new claims do not suggest a specific machine or AI model is required. Simply implementing the abstract idea on a generic computer or using a computer as a tool to perform an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the present claims are ineligible for patent.
Claim Rejection – 35 U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-9, 20, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sreenivasan (Pub. No.: US 2022/0237700), in view of Xie et al. (Pub. No.: US 2022/0108395) and Yendigeri et al. (Pub. No.: US 2023/0141408).
As per claim 1, Sreenivasan teaches a method for performing investment portfolio balancing, the method comprising:
receiving, by a processor, data associated with a user (see paragraph 0214-0218, prior art teaches receiving user risk tolerance data and user financial goal data in the form of questionnaire; see paragraph 0223, prior art teaches receiving personal data from the user; also see paragraph 0228-0229, “the AI investment platform is operable to link at least one existing portfolio to a user profile…This allows users to maintain one or more of the at least one existing portfolio (e.g., 401k) on a different platform (e.g., external advisory and/or brokerage firm) while allowing the AI investment platform to optimize the one or more of the at least one existing portfolio”; see paragraph 0230, prior art teaches providing a GUI for importing user’s existing portfolio),
the data comprising at least one of user portfolio data, user risk profile data, or user financial goal data (see paragraph 0217, “the platform is better able to imitate the desired investment decision-making of the user and to more accurately build a risk profile”; see paragraph 0219, “FIG. 46 provides an investment recommendations GUI including a pie chart representing the recommended investment plan, a total investment amount, a risk profile rating…”; also see paragraph 0230 and 0249, “the platform includes an artificial intelligence module operable to assess the portfolio (i.e., a portfolio assessment module) based on the user’s risk profile”; also see paragraph 0020 and 0217 and FIG. 39-45 for receiving user’s financial goals);
receiving, by the processor, market condition information (see abstract, “an artificial intelligence (AI) platform operable to generate securities portfolio recommendations, including an AI engine operable to evaluate market sentiment data”; see paragraph 0238, “The signal generation module receives data, including current market data (e.g., price and/or volume of trades of a plurality of securities), macroeconomic data (e.g., unemployment rate, labor participation rate, changes in gross domestic product (GDP), inflation rate, etc.), fundamental data, interest rate data, and/or sentimental data”; also see paragraph 0281-0282) from a plurality of real-time data sources (see paragraph 0252 and 0264);
generating, by the processor, a plurality of investment recommendations using a first artificial intelligence (AT) model with the data and the market condition information as input, wherein the first AI model comprises a generative AI model configured to evaluate market conditions against the user’s risk profile and investment goals to produce recommendations specific to the user (see paragraph 0017, 0215, 0219, and 0230, “manage the imported portfolio (meaning that the AI investment platform produces recommendations for how to change or maintain the portfolio to better match the user’s risk profile, or both monitor and manage the imported portfolio”);
ranking, by the processor, the plurality of investment recommendations based on coherence and accuracy using at least one second AI model, wherein the at least one second AI model evaluates each recommendation and assigns a quality score (see paragraph 0250, “The platform presents one or more suggested optimizations of the one or more linked accounts and assigns an overall score to each suggested optimization (e.g., excellent, good, needs improvement, etc.)…the system presents each recommendation individually or as a whole and assigns a rating to each individual recommendation or to the recommendations as a whole”; scoring each suggested optimizations is the same as ranking the plurality of investment recommendations; see paragraph 0274, “By using the predicted rankings and current market indicators, the AI investment platform generates a score (e.g., Tenjin score) for each stock in the AI investment platform, with a higher score indicating a better stock”; see paragraph 0018-0021, “the server generates a suggested portfolio security allocation based on a weighted aggregation of the recommendation data generated by each of the plurality of distinct artificial intelligence modules”, prior art teach using a plurality of distinct artificial intelligence modules for analyzing investment recommendations, and the server which acts as a separate AI ranks and scores each recommendations generated by other AI);
generating, by the processor, finalized investment recommendations based on ranked plurality of investment recommendations (see paragraph 0250, “The platform presents one or more suggested optimizations of the one or more linked accounts and assigns an overall score to each suggested optimization (e.g., excellent, good, needs improvement, etc.)…As a result, for all assets and/or holdings in the linked account portfolio, advice for asset replacement, weight distribution, and other optimization tools are provided”); and
presenting, by the processor, the finalized investment recommendations to the user for selection via a graphical user interface on a user device (see paragraph 0250, “The platform presents one or more suggested optimizations of the one or more linked accounts and assigns an overall score to each suggested optimization (e.g., excellent, good, needs improvement, etc.)…As a result, for all assets and/or holdings in the linked account portfolio, advice for asset replacement, weight distribution, and other optimization tools are provided”; prior art presents a plurality of suggested optimizations – or investment recommendations – each has an assigned score, and one skilled in the art would recognize that the one with the highest score should be taken as the finalized suggested optimization; see paragraph 0261, “the AI investment provides the investor an option to apply suggested changes to the portfolio”);
in response to a single user selection input, automatically executing portfolio rebalance orders in accordance with the selected finalized investment recommendations without requiring further user input (see paragraph 0261, “the AI investment provides the investor an option to apply suggested changes to the portfolio”);
wherein selection of the finalized investment recommendations rebalances an investment portfolio of the user (see paragraph 0220, “The investment account generation and/or linkage GUI…thereby allowing users to being acting on the recommendations provided by the platform”; see paragraph 0258, “the AI investment platform requires user approval to adjust the assets in the portfolio (e.g., based on user preferences)”; see paragraph 0261, “the AI investment provides the investor an option to apply suggested changes to the portfolio”).
Examiner notes, Sreenivasan teaches comparing portfolio performance before and after optimization (see paragraph 0249), but the prior art does not explicitly teach analyzing and displaying, by the processor, proportions of each industry sector in the investment portfolio before and after rebalancing based on the finalized investment recommendations to provide a visual comparison of sector allocation on the user device.
Xie teaches analyzing and displaying, by the processor, proportions of each industry sector in the investment portfolio before and after rebalancing based on the finalized investment recommendations to provide a visual comparison of sector allocation on the user device (see paragraph 0128, “A display to a user may show the portfolio’s exposures by sector as well as financed emission by sector, before and after, and during all periods of, the optimization”, optimization is the same as rebalancing; see paragraph 0132, “display the portfolio composition of the institution by category (e.g., industry, sector, counterparty, etc.) before and after the change provided by the user in box 700).
It would have been obvious to one of ordinary skill in the art on the effective filing date of the present application to modify Sreenivasan with teaching from Xie to include analyzing and displaying, by the processor, proportions of each industry sector in the investment portfolio before and after rebalancing based on the finalized investment recommendations to provide a visual comparison of sector allocation on the user device. The modification would have been obvious, because it is merely applying a known technique (i.e., displaying the before and after portfolio composition by sector) to a known method (i.e., performing investment portfolio balancing) ready to provide predictable result (i.e., provide visual representation of portfolio before and after balancing for user to quickly digest the information and confirm the balancing).
Examiner notes the combination of Sreenivasan and Xie does not explicitly teach cross-verifying the ranked plurality of investment recommendations using at least one third AI model and selecting top-ranked recommendations based on the quality scores.
Yendigeri teaches cross-verifying the ranked plurality of investment recommendations using at least one third AI model and selecting top-ranked recommendations based on the quality scores (see paragraph 0071, with the one or more machine learning models, to generate the recommendations for the problem of the client and confidence scores for the recommendations”; see paragraph 0072, “process 500 may include processing the recommendations and the confidence scores, with a natural language generation model, to generate a solution to the problem of the client and content for the solution”; also see FIG. 5; prior art teaches using a separate AI model to process the recommendations and their corresponding confidence score to finalize a recommendation to user).
It would have been obvious to one of ordinary skill in the art on the effective filing date of the present application to modify the combination of Sreenivasan and Xie with teaching from Yendigeri to include cross-verifying the ranked plurality of investment recommendations using at least one third AI model and selecting top-ranked recommendations based on the quality scores. The modification would have been obvious, because it is merely applying a known technique (i.e., using an AI model to process recommendations generated by other AI models) to a known method (i.e., performing investment portfolio balancing) ready to provide predictable result (i.e., use a combination of AI models trained to perform specialized tasks to increase efficiency).
As per claim 2, Sreenivasan teaches wherein the at least one second AI model is a generative AI model (see paragraph 0218, “The AI investment platform is operable to create at least one financial goal portfolio to a user profile”; see paragraph 0275, “the AI investment platform generates the model using stocks listed on at least one stock market index”; clearly, the AI in prior art can generate investment recommendations, investment models, and optimized portfolio, thus the AI is a generative AI model).
As per claim 3, Sreenivasan teaches wherein the at least one third AI model is a generative AI model (see paragraph 0218, “The AI investment platform is operable to create at least one financial goal portfolio to a user profile”; see paragraph 0275, “the AI investment platform generates the model using stocks listed on at least one stock market index”; clearly, the AI in prior art can generate investment recommendations, investment models, and optimized portfolio, thus the AI is a generative AI model).
As per claim 4, Sreenivasan teaches wherein the processor is configured to present the finalized investment recommendations to the user by graphically displaying the investment portfolio and a portfolio allocated based on the finalized investment recommendations on the user device (see paragraph 0018, “server generates a suggested portfolio securities allocation based on weighted aggregation of the recommendation data generated by each of the plurality of distinct artificial intelligence modules”; see paragraph 0293, “The asset allocation GUI includes, but is not limited to, a chart illustrating a percentage of assets (e.g., by sector), a percentage of each asset in the custom portfolio (e.g., by sector)”).
As per claim 5, Sreenivasan teaches monitoring, by the processor, a time period since last portfolio rebalancing; and for the time period exceeding a predetermined rebalancing time threshold, generating, by the processor, a rebalancing alert on a user device (see paragraph 0278, “the AI investment platform determine whether…(4) it is time to rebalance the portfolio (e.g., predetermined time interval has passed). If any of the four scenarios occur, the AI investment platform performs portfolio optimization”; also see paragraph 0258, “the AI investment platform automatically provides a notification (e.g., via a mobile application) regarding the portfolio optimization”; also see paragraph 0293 for alert).
As per claim 6, Sreenivasan teaches monitoring, by the processor, a performance level of the investment portfolio; and for the performance level deviating from a portfolio performance threshold, generating, by the processor, a rebalancing alert on a user device (see paragraph 0278, “the AI investment platform determine whether (1) actual volatility is greater than desired volatility, (2) actual returns are less than desired returns… If any of the four scenarios occur, the AI investment platform performs portfolio optimization”; also see paragraph 0258, “the portfolio optimization of a portfolio is triggered after a threshold is exceeded between an actual portfolio value and a target portfolio value…the AI investment platform automatically provides a notification (e.g., via a mobile application) regarding the portfolio optimization”; also see paragraph 0293 for alert).
As per claim 7, Sreenivasan teaches receiving, by the processor, at least one rebalance preference comprising one or more of target allocations, a rebalancing frequency, or at least one threshold-based rebalancing trigger (see paragraph 0261, “For portfolios managed by the AI investment platform, the optimization occurs based on preferences in the user account”; see paragraph 0277, “the AI investment platform automatically and/or autonomously adjusts (e.g., buy assets, sell assts) at least one portfolio when a threshold is exceeded between an actual portfolio and a target portfolio. In one embodiment, the threshold is manually set by the user”; see paragraph 0258 and 0299).
As per claim 8, Sreenivasan teaches executing, by the processor, a user request in response to a single user input to a user device to select the finalized investment recommendations, wherein executing the user request comprises automatically submitting at least one order, in accordance with selected finalized investment recommendations without requiring further input from the user (see paragraph 0250, “The platform presents one or more suggested optimizations of the one or more linked accounts and assigns an overall score to each suggested optimization (e.g., excellent, good, needs improvement, etc.)…As a result, for all assets and/or holdings in the linked account portfolio, advice for asset replacement, weight distribution, and other optimization tools are provided”; prior art presents a plurality of suggested optimizations – or investment recommendations – each has an assigned score, and one skilled in the art would recognize that the one with the highest score should be taken as the finalized suggested optimization; see paragraph 0261, “the AI investment provides the investor an option to apply suggested changes to the portfolio”).
As per claim 9, Sreenivasan teaches receiving, by an input layer of the first AI model, at least one of user preferences, investment goals, or risk profile of the user; and generating, by the first AI model, the plurality of investment recommendations by evaluating the data and the market condition information based on the at least one of user preferences, investment goals, risk profile of the user (see paragraph 0201, 0205, 0217-0218, and 0277).
As per claim 20, Sreenivasan teaches wherein the first AI model comprises at least one of a recurrent neural network (RNN), a deep recurrent neural network (DRNN), a long short-term memory (LSTM) network, a convolutional neural network (CNN), a fully connected neural network, or a large multimodal language model configured to process different types of input data (see paragraph 0265-0266).
As per claim 22, Sreenivasan teaches wherein the first AI model and the at least one second AI model are iteratively trained using historical data as training input, and wherein training parameters are adjusted to generate optimal results (see paragraph 0255 and 0266).
Claim(s) 10-11, 13-16, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sreenivasan (Pub. No.: US 2022/0237700), in view of Xie et al. (Pub. No.: US 2022/0108395) and Son et al. (KR 102104316 B1).
As per claim 10 and 15, Sreenivasan teaches a method for performing investment portfolio adjustment, the method comprising:
receiving, by a processor, user portfolio associated with a user from a portfolio database (see paragraph 0214-0218, prior art teaches receiving user risk tolerance data and user financial goal data in the form of questionnaire; see paragraph 0223, prior art teaches receiving personal data from the user; also see paragraph 0228-0229, “the AI investment platform is operable to link at least one existing portfolio to a user profile…This allows users to maintain one or more of the at least one existing portfolio (e.g., 401k) on a different platform (e.g., external advisory and/or brokerage firm) while allowing the AI investment platform to optimize the one or more of the at least one existing portfolio”; see paragraph 0230, prior art teaches providing a GUI for importing user’s existing portfolio);
receiving, by the processor, a plurality of news data from various real-time information providers, the news data comprising at least financial news, market sentiment data, and macroeconomic indicators (see abstract, “an artificial intelligence (AI) platform operable to generate securities portfolio recommendations, including an AI engine operable to evaluate market sentiment data”; see paragraph 0238, “The signal generation module receives data, including current market data (e.g., price and/or volume of trades of a plurality of securities), macroeconomic data (e.g., unemployment rate, labor participation rate, changes in gross domestic product (GDP), inflation rate, etc.), fundamental data, interest rate data, and/or sentimental data”; also see paragraph 0281-0282);
analyzing and weighing, by the processor, the plurality of news data based on the user portfolio using an artificial intelligence (AI) model, wherein the AI model assigns impact weights to individual news items based on relevance to specific securities and sectors within the user portfolio (see paragraph 0018-0021, “server generates a suggested portfolio securities allocation based on weighted aggregation of the recommendation data generated by each of the plurality of distinct artificial intelligence modules”; see paragraph 0249-0250 analyzing weight of investment in portfolio; see paragraph 0263, “The AI investment platform is operable to determine weightings for the ensemble engine…some assets (e.g., stocks) have more weighting for sentiments (e.g., Tesla), while other assets have more weighting for fundamental analysis (e.g., Pfizer)”); and
adjusting, by the processor, the user portfolio based on a weighed plurality of news data (see paragraph 0258, “the AI investment platform automatically adjusts assets in the portfolio based on the portfolio optimization”; also see paragraph 0277, “the automatic and/or the autonomous adjustment of the at least one portfolio is based on output from the optimization engine”),
wherein the processor is configured to adjust the user portfolio by: generating one or more portfolio adjustment recommendations by the AI model, each recommendation comprising specific buy, sell, or hold actions (see paragraph 0229, “the AI investment platform advises buying a first stock a selling a second stock for the at least one existing portfolio”; see paragraph 0249, “a weighted score (e.g., buy/hold/sell) for the portfolio”; see paragraph 0250, “The platform presents one or more suggested optimizations of the one or more linked accounts and assigns an overall score to each suggested optimization (e.g., excellent, good, needs improvement, etc.)…As a result, for all assets and/or holdings in the linked account portfolio, advice for asset replacement, weight distribution, and other optimization tools are provided”; also see paragraph 0279-0280); and
transmitting the one or more portfolio adjustment recommendations to a user device for the user to review and select (see paragraph 0250, “The platform presents one or more suggested optimizations of the one or more linked accounts and assigns an overall score to each suggested optimization (e.g., excellent, good, needs improvement, etc.)…As a result, for all assets and/or holdings in the linked account portfolio, advice for asset replacement, weight distribution, and other optimization tools are provided”; prior art presents a plurality of suggested optimizations – or investment recommendations – each has an assigned score, and one skilled in the art would recognize that the one with the highest score should be taken as the finalized suggested optimization; see paragraph 0261, “the AI investment provides the investor an option to apply suggested changes to the portfolio”).
Examiner notes, Sreenivasan teaches comparing portfolio performance before and after optimization (see paragraph 0249), but the prior art does not explicitly teach analyzing and displaying proportions of each industry sector in the investment portfolio before and after rebalancing based on the one or more portfolio adjustment recommendations to provide a comparison of sector allocation.
Xie teaches analyzing and displaying proportions of each industry sector in the investment portfolio before and after rebalancing based on the one or more portfolio adjustment recommendations to provide a comparison of sector allocation (see paragraph 0128, “A display to a user may show the portfolio’s exposures by sector as well as financed emission by sector, before and after, and during all periods of, the optimization”, optimization is the same as rebalancing; see paragraph 0132, “display the portfolio composition of the institution by category (e.g., industry, sector, counterparty, etc.) before and after the change provided by the user in box 700).
It would have been obvious to one of ordinary skill in the art on the effective filing date of the present application to modify Sreenivasan with teaching from Xie to include analyzing and displaying proportions of each industry sector in the investment portfolio before and after rebalancing based on the one or more portfolio adjustment recommendations to provide a comparison of sector allocation. The modification would have been obvious, because it is merely applying a known technique (i.e., displaying the before and after portfolio composition by sector) to a known method (i.e., performing investment portfolio balancing) ready to provide predictable result (i.e., provide visual representation of portfolio before and after balancing for user to quickly digest the information and confirm the balancing).
To support the argument that AI model assigns impact weights to individual news items based on relevance to specific securities and sectors within the user portfolio was known prior to the present invention, Examiner cites Son et al.
Son teaches AI model assigns impact weights to individual news items based on relevance to specific securities and sectors within the user portfolio (see abstract, “An apparatus for predicting a stock price of a company by analyzing news…inputting at least one word extracted from the newly uploaded news as an input value of a news analysis neural network matched to the first news classification group calculate the positive, neutral, and negative impact values of the newly uploaded news”).
It would have been obvious to one of ordinary skill in the art on the effective filing date of the present application to modify the combination of Sreenivasan and Xie with teaching from Son to include AI model assigns impact weights to individual news items based on relevance to specific securities and sectors within the user portfolio. The modification would have been obvious, because it is merely applying a known technique (i.e., using AI to assign impact weight to each news article) to a known method (i.e., performing investment portfolio balancing) ready to provide predictable result (i.e., provide real-time portfolio recommendations).
As per claim 11 and 16, Sreenivasan teaches wherein the AI model is a generative AI model (see paragraph 0218, “The AI investment platform is operable to create at least one financial goal portfolio to a user profile”; see paragraph 0275, “the AI investment platform generates the model using stocks listed on at least one stock market index”; clearly, the AI in prior art can generate investment recommendations, investment models, and optimized portfolio, thus the AI is a generative AI model).
Claim 12 and 17 are cancelled.
As per claim 13 and 18, Sreenivasan teaches executing, by the processor, a user request in response to a single user input to the user device to select the one or more portfolio adjustment recommendations, wherein the executing the user request comprises automatically submitting at least one order, in accordance with selected one or more portfolio adjustment recommendations without requiring further input from the user (see paragraph 0258, “In one embodiment, the AI investment platform automatically adjusts assets in the portfolio based on the portfolio optimization…In one embodiment, the AI investment platform requires user approval to adjust the assets in the portfolio”).
As per claim 14 and 19, Sreenivasan teaches wherein the select the one or more portfolio adjustment recommendations comprising performing, by the user, at least one of response selection, response modification, or additional response request in association with the one or more portfolio adjustment recommendations (see paragraph 0250, “The platform presents one or more suggested optimizations of the one or more linked accounts and assigns an overall score to each suggested optimization (e.g., excellent, good, needs improvement, etc.)…As a result, for all assets and/or holdings in the linked account portfolio, advice for asset replacement, weight distribution, and other optimization tools are provided”; prior art presents a plurality of suggested optimizations – or investment recommendations – each has an assigned score, and one skilled in the art would recognize that the one with the highest score should be taken as the finalized suggested optimization; see paragraph 0261, “the AI investment provides the investor an option to apply suggested changes to the portfolio”)
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sreenivasan (Pub. No.: US 2022/0237700), in view of Xie et al. (Pub. No.: US 2022/0108395) and Yendigeri et al. (Pub. No.: US 2023/0141408), and further in view of D’Alessandro (Pub. No.: US 2022/0068274).
As per claim 21, Sreenivasan teaches ranking, by the processor, the plurality of investment recommendations based on coherence and accuracy using at least one second AI model (see paragraph 0250, “The platform presents one or more suggested optimizations of the one or more linked accounts and assigns an overall score to each suggested optimization (e.g., excellent, good, needs improvement, etc.)…the system presents each recommendation individually or as a whole and assigns a rating to each individual recommendation or to the recommendations as a whole”; scoring each suggested optimizations is the same as ranking the plurality of investment recommendations). However, Sreenivasan does not explicitly teach wherein the at least one second AI model comprises at least one of a generative adversarial network (GAN), a variational auto-encoder (VAE), an auto-regressive model, or a transformer, and wherein an input layer of the at least one second AI model receives the plurality of investment recommendations, a hidden layer performs the ranking based on coherence and accuracy, and an output layer outputs the ranked plurality of investment recommendations.
D’Alessandro teaches at least one second AI model comprises at least one of a generative adversarial network (GAN), a variational auto-encoder (VAE), an auto-regressive model, or a transformer, and wherein the AI model comprises an input layer, a hidden layer, and an output layer (see paragraph 0129 and 0135).
It would have been obvious to one of ordinary skill in the art on the effective filing date of the present application to modify Sreenivasan with teaching from D’Alessandro to include the at least one second AI model comprises at least one of a generative adversarial network (GAN), a variational auto-encoder (VAE), an auto-regressive model, or a transformer, and wherein an input layer of the at least one second AI model receives the plurality of investment recommendations, a hidden layer performs the ranking based on coherence and accuracy, and an output layer outputs the ranked plurality of investment recommendations. The modification would have been obvious, because it is merely applying a known technique (i.e., using conventional generative model) to a known method (i.e., performing investment portfolio balancing) ready to provide predictable result (i.e., use conventional AI model to provide real-time recommendation).
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sreenivasan (Pub. No.: US 2022/0237700), in view of Xie et al. (Pub. No.: US 2022/0108395) and Son et al. (KR 102104316 B1), and further in view of D’Alessandro (Pub. No.: US 2022/0068274).
As per claim 23, Sreenivasan does not teach the AI model comprises a generative AI model having an input layer that receives the plurality of news data, a hidden layer that processes the plurality of news data to assign the impact weights based on relevance to the user portfolio, and an output layer that outputs the assigned impact weights.
Son teaches AI model assigns impact weights to individual news items based on relevance to specific securities and sectors within the user portfolio (see abstract, “An apparatus for predicting a stock price of a company by analyzing news…inputting at least one word extracted from the newly uploaded news as an input value of a news analysis neural network matched to the first news classification group calculate the positive, neutral, and negative impact values of the newly uploaded news”).
D’Alessandro teaches a generative AI model having an input layer, a hidden layer, and an output layer (see paragraph 0129 and 0135).
It would have been obvious to one of ordinary skill in the art on the effective filing date of the present application to modify Sreenivasan with teaching from Son and D’Alessandro to include AI model comprises a generative AI model having an input layer that receives the plurality of news data, a hidden layer that processes the plurality of news data to assign the impact weights based on relevance to the user portfolio, and an output layer that outputs the assigned impact weights. The modification would have been obvious, because it is merely applying a known technique (i.e., using conventional generative model) to a known method (i.e., performing investment portfolio balancing) ready to provide predictable result (i.e., use conventional AI model to provide real-time recommendation).
Response to Argument
Rejection under 35 U.S.C. 101
Applicant's arguments filed 02/26/2026 have been fully considered but they are not persuasive.
Step 2A Prong One
Applicant argued that the amended claims “recite a specific multi-stage AI architecture with distinct technical components: (1) a first AI model comprising a generative AI model configured to evaluate market conditions against the user’s risk profile and investment goals to produce recommendations specific to the user; (2) a second AI model that evaluates each recommendation and assigns a quality score based on coherence and accuracy; (3) a third AI model for cross-verification of the ranked recommendations; and (4) a sector allocation analysis and visual display module that computes and presents before-and-after industry sector proportions on a user device”. Applicant argued that the claims are not directed to a method of organizing human activity or a mental process. Examiner disagrees and points out that the AI is merely an additional element to the abstract concept of performing investment portfolio balancing. AI model is recited in high level of generality – the claim language merely recites four AI models performing specific jobs: evaluate market conditions against user risk profiles and produce recommendations, evaluate each recommendation and assign a quality score, cross-verification of ranked recommendations, and perform sector allocation analysis and presents before-and-after industry sector proportions. Individually, these tasks are abstract concepts unrelated to improving computer function and can be performed in the human mind. As a combination, they are still directed to an abstract concept of “investment portfolio rebalancing”.
Step 2A Prong Two
Applicant argued that the present claims improve the functioning of a computer or technology field integrate a judicial exception into a practical application. Applicant merely argued that the multi-model arrangement improves function beyond any single model can achieve. Examiner disagrees and points out that the combination of separate AI models was not new at the time of invention – using a combination of Small Language Models (SLM) each specialized a specific task rather than a Large Language Model (LLM) was well-known in the field of artificial intelligent. SLMs have fewer parameters, offering higher efficiency and specialized, lower-cost performance, and capable of being run on low-end hardware. LLM, on the other hand, have massive parameters, provide superior, broad-spectrum intelligence but require significant cloud computing resources. One skilled in the art at the time of filing of the present application, would know the pros and cons of SLM and LLM, and would be able to choose an appropriate arrangement of AI for the job. Moreover, paragraph 0032 and 0034 of the specification list some existing AI models, but also states the AI model may not be limited to the listed models. Clearly, the claimed invention merely applies existing AI technology in portfolio balancing environment. Therefore, the recitation of a combination of SLM AI models does not improve computer or AI function.
Applicant also argued the real-time visualization provides technical improvement in graphical user interface. Examiner disagrees and points to Electric Power Group v. Alstom, where the court found that displaying result of calculations in real-time require nothing other than off-the-shelf general-purpose computers. Applicant did not provide persuasive rationale to support the argument that the amended claims provide technical improvement in GUI.
Applicant argued that the amended claims are fundamentally different from the claims in Recentive Analytics v. Fox Corp. In the Recentive Analytics v. Fox Corp case, the Federal Circuit ruled patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible. Applicant argued that the amended claims “delineate a specific multi-model pipeline”. As discussed earlier, multi-model pipeline was not new at the time of invention – using a combination of Small Language Models (SLM) each specialized a specific task rather than a Large Language Model (LLM) was well-known in the field of artificial intelligent. The present claims are merely implementing an abstract concept using existing arrangement of SLMs. Therefore, the recitation of multi-model pipeline does not improve computer or AI function.
Step 2B
Applicant argued that the ordered combination of elements provides significantly more under Step 2B. Examiner disagrees. As discussed earlier, the combination of AI models merely performs analytical tasks that can be done mentally. Using a combination of SLMs instead of a LLM to perform specific calculations was a well-understood, routine, and conventional. The combination of AI models does not produce any technological benefit over what was already known.
New Claim 20 recites the first AI model can be neural network including a recurrent neural network, a deep recurrent neural network, a long short-term memory network, a convolutional neural network, a fully connected neural network or a large multimodal language model configured to process different types of input data. The claim language is unspecific and lists well-known neural networks in the field. Similarly, new claim 21 recites the second AI model can be generative adversarial network, a variational auto-encoder, an auto-regressive model, or a transfer. The claim language is unspecific and lists well-known models in the field. Claim 22 recites AI models are iteratively trained using historical data as training input, but this is how most AI models are trained. Claim 23 recites input-layer, hidden-layer, and output-layer, but this arrangement is the same as conventional AI models. The new claims do not suggest a specific machine or AI model is required.
Simply implementing the abstract idea on a generic computer or using generic AI as a tool to perform an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the present claims are ineligible for patent. Examiner maintains the ground of rejection under 35 U.S.C. 101.
Rejection under 35 U.S.C. 103
Examiner cites Yendigeri et al. (Pub. No.: US 2023/0141408), Son et al. (KR 102104316 B1) and D’Alessandro (Pub. No.: US 2022/0068274) to address the amended claims. Updated rejection is provided in this Office Action.
Conclusion
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/HAO FU/Primary Examiner, Art Unit 3695
MAY-2026
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