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
Claims 1-8 and 10-17 are currently pending and rejected.
Claims 9 and 18 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. 18/638,518 (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 21 of copending Application No. 18/638,518 (reference application). Although the claims at issue are not identical, they recite the same limitations with slightly different wordings.
Claim 3 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/638,518 (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. 18/638,518 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 5 and 14 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 5 of copending Application No. 18/638,518 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 6 and 15 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 6 of copending Application No. 18/638,518 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 7 and 16 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 7 of copending Application No. 18/638,518 (reference application). Although the claims at issue are not identical, every limitation in the present claim is taught by copending claim.
Claim 8 and 17 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/638,518 (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. 18/638,518 (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 21 of copending Application No. 18/638,518 (reference application). Although the claims at issue are not identical, they recite the same limitations with slightly different wordings.
Claim 12 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/638,518 (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 1-8 and 10-17 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 1-8 and 10-17 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Step 1: The claims 1-8 and 10-17 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 1-8 are directed to a method (i.e., a process), and claims 10-17 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 recites a processor as additional element. Dependent claims 2-8 do not recite any other additional element. Independent claim 10 recites a database and a processor as additional elements. Dependent claims 11-17 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 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 2-8 and 11-17 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. 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 and 3-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sreenivasan (Pub. No.: US 2022/0237700), in view of Liu (Patent No.: 10,380,613).
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, real-time market condition information from a plurality of real-time data sources (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”; see paragraph 0252, “The AI investment platform is operable to obtain information and/or data…in real time or near-real time”; see paragraph 0296, “the at least one watchlist provides real time updates to a value of the at least one asset”; also see paragraph 0281-0282);
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 (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”; see paragraph 0257-0261, “The portfolio optimization is preferably conducted in real time and/or near-real-time based on changes in the market”),
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 against predefined coherence metrics to detect inconsistencies 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 by selecting top-ranked recommendations from the 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”).
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 does not explicitly teach the at first AI model, the at least one second AI model, and at least one third AI model used for cross-verification operate as a multi-stage AI pipeline in which the output of each stage serves as input to the text stage, and wherein the multi-stage AI pipeline improves the technical accuracy and reliability of AI-generated investment recommendations by detecting and filtering inconsistent or low-quality AI outputs before presentation to the user.
Yendigeri teaches the at first AI model, the at least one second AI model, and at least one third AI model used for cross-verification operate as a multi-stage AI pipeline in which the output of each stage serves as input to the text stage, and wherein the multi-stage AI pipeline improves the technical accuracy and reliability of AI-generated investment recommendations by detecting and filtering inconsistent or low-quality AI outputs before presentation to the user (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 the at first AI model, the at least one second AI model, and at least one third AI model used for cross-verification operate as a multi-stage AI pipeline in which the output of each stage serves as input to the text stage, and wherein the multi-stage AI pipeline improves the technical accuracy and reliability of AI-generated investment recommendations by detecting and filtering inconsistent or low-quality AI outputs before presentation to the user. 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 3, Sreenivasan does not teach the third AI model used for cross-verification comprises a generative AI model, and wherein the cross-verifying detects inconsistences between the ranked recommendations and filters recommendations having quality scores below a predetermined threshold.
Yendigeri teaches the third AI model used for cross-verification comprises a generative AI model, and wherein the cross-verifying detects inconsistences between the ranked recommendations and filters recommendations having quality scores below a predetermined threshold (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 Sreenivasan with teaching from Yendigeri to include the third AI model used for cross-verification comprises a generative AI model, and wherein the cross-verifying detects inconsistences between the ranked recommendations and filters recommendations having quality scores below a predetermined threshold. 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 4, Sreenivasan teaches wherein the graphical user interface displays the investment portfolio and a portfolio allocated based on the finalized investment recommendations as comparative visualizations (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)”), wherein selection of the finalized investment recommendations is performed by the user on the user device (see paragraph 0261, “the AI investment provides the investor an option to apply suggested changes to the portfolio”).
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 wherein the automatically executing portfolio rebalancing orders comprises automatically submitting at least one order to one or more brokers or robo-advisory platform in accordance with selected finalized investment recommendations (see paragraph 0019, “the server is operable to automatically and autonomously buy and/or sell securities in the at least one investment account based on weighted aggregation of the recommendation data”; also see paragraph 0198).
Claim 9 is cancelled.
Claim(s) 2 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 2, 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 a generative AI model selected from the group consisting of generative adversarial networks (GANs), variational auto-encoders (VAEs), auto-regressive models and transformers.
D’Alessandro teaches the at least one second AI model comprises a generative AI model selected from the group consisting of generative adversarial networks (GANs), variational auto-encoders (VAEs), auto-regressive models and transformers (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 a generative AI model selected from the group consisting of generative adversarial networks (GANs), variational auto-encoders (VAEs), auto-regressive models and transformers. 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) 10 and 13-17 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, 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)”);
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 with associated confidence scores (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”),
wherein AI model continuously processes incoming news data in real-time and dynamically adjusts impact weights based on observed correlation between news events and subsequent market movement, thereby improving the technical accuracy of portfolio adjustment recommendations over time (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).
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 13, Sreenivasan teaches present the one or more portfolio adjustment recommendations to the user by graphically displaying the user portfolio and a portfolio allocated based on the one or more adjustment recommendations on the user device, wherein selection of the one or more portfolio adjustment recommendations is performed by the user 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)”), wherein selection of the finalized investment recommendations is performed by the user on the user device (see paragraph 0261, “the AI investment provides the investor an option to apply suggested changes to the portfolio”).
Claim 14 is rejected for the same reason as in claim 5.
Claim 15 is rejected for the same reason as in claim 6.
Claim 16 is rejected for the same reason as in claim 7.
Claim 17 is rejected for the same reason as in claim 8.
Claim(s) 11 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 11, 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 a generative AI model selected from the group consisting of generative adversarial networks (GANs), variational auto-encoders (VAEs), auto-regressive models and transformers.
D’Alessandro teaches the at least one second AI model comprises a generative AI model selected from the group consisting of generative adversarial networks (GANs), variational auto-encoders (VAEs), auto-regressive models and transformers (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 a generative AI model selected from the group consisting of generative adversarial networks (GANs), variational auto-encoders (VAEs), auto-regressive models and transformers. 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) 12 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 Yendigeri et al. (Pub. No.: US 2023/0141408).
As per claim 12, Sreenivasan does not teach cross-verify the one or more portfolio adjustment recommendations using at least one additional AI model comprising an independent AI model or generative AI model before transmitting the one or more portfolio adjustment recommendations to the user device.
Yendigeri teaches cross-verify the one or more portfolio adjustment recommendations using at least one additional AI model comprising an independent AI model or generative AI model before transmitting the one or more portfolio adjustment recommendations to the user device (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-verify the one or more portfolio adjustment recommendations using at least one additional AI model comprising an independent AI model or generative AI model before transmitting the one or more portfolio adjustment recommendations to the user device. 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).
Response to Argument
Rejection under 35 U.S.C. 101
Applicant's arguments filed 02/27/2026 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. 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
Applicant amended independent claim 1 and 10 by adding the limitation, “AI model interprets and weights the real-time market condition information to adjust portfolio positions while taking the risk profile of the user into consideration”. Applicant argued that Sreenivasan does not teach this limitation. Examiner disagrees and points out that the prior art actually teaches the amended feature. Examiner has updated the rejection by citing the paragraphs where the amended feature is taught. To further support the argument that the amended feature was known prior to the filing of the present application, Examiner cites a new prior art Liu (Patent No.: 10,380,613).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAO FU whose telephone number is (571)270-3441. The examiner can normally be reached 9:00 AM - 6:00 PM PST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine M Behncke can be reached at (571) 272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HAO FU/Primary Examiner, Art Unit 3695
MAY-2026
Prosecution Insights