DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6 July 2026 has been entered.
The following is a final office action in response to the application filed 6 July 2026.
Applicant’s amendments to Claims 1, 5, 14 and 18 have been received and are acknowledged. Claims 7 and 20 were previously cancelled.
The applicant's claim for benefit of provisional application US 63/521,316 filed 06/15/2023 and US 63/584,738 filed 09/22/2023, has been received and acknowledged.
Claims 1-6, 8-19, and 21-26 are currently pending and have been examined.
Response to Arguments
Applicant's arguments filed 6 July 2026 have been fully considered but they are not persuasive.
With regard to the rejections under 35 USC 101, Applicant argues: (1) That the recited Claims 1-6, 8-19, and 21-26 when considered as a whole do not recite an abstract idea. Applicant asserts that exemplary Claim 1 as recited does not fall into any of the enumerated categories. (Applicant’s response, 9-10) (2) Applicant further argues that the recited amended Claim 1 “relate to arrangements and functions for components of a computing device configured for specialized operations for dynamically training a machine learning model for asset portfolio generation and trade execution… The re-trained model determines an updated asset portfolio, and trades are automatically executed by forming an order and transmitting a programmatic call associated with the order to a trading server computer. Thus, the claims are tied to a specific combination of training data, output-head probability prediction, portfolio determinization, adaptive re-training, and programmatic trade execution, rather than merely monopolizing the generic idea of "using continuous machine learning model optimization to generate artificial intelligence managed portfolios…” (Applicant’s response, 10). (3) Referencing Desjardins and the Kim memo, Applicant further asserts that the instant recited claims integrate the judicial exception “… into a practical application of a machine-learning system that generates and executes asset portfolios…” (Applicant’s response, 10 -11). Applicant asserts that the newly amended claim limitations “…differ in numerous details…” from those previously rejected in the 12 May 2026 Office Action. Referencing various claim limitations, Applicant asserts the “specific recitations…extend beyond the .. proposed judicial exceptions…” that “…demonstrates how the claimed method offers a technical improvement over prior systems…performs a precise model-level transformation by using a plurality of output heads to convert respective candidate-asset factor values into probabilities that the candidate assets should be included in an asset portfolio…architectural limitation directly improves the operation of the machine- learning portfolio-generation system by enabling candidate-asset evaluation, portfolio formation, performance-based re-training, and automated trade execution to proceed through a defined technical workflow. Specification, [0051], [0070] and [0071]… these recitations are not merely "generic" computer elements that implement abstract ideas on a computer; the recitations instead specify particular techniques for improving a machine-learning system, improving operation performance….” (Applicant’s response, 12-13). (4) Applicant further references the Kim memo, Desjardins, Enfish, McRo and asserts that the “…specification frames the relevant technical problems as those of optimizing machine-learning models…” and that is “…solved by the new machine-learning system described in the disclosure…..the present embodiments improve machine- learning models, model operations, and task execution efficiency.” (Applicant’s response, 13-16). (5) Applicant additionally asserts that the recited limitations are “significantly more than the abstract idea” because “…technical elements in the claims effectively tie data synthesis, output-head probability prediction, portfolio determinization, and adaptive model re-training to an improved machine-learning system, thereby providing significantly more than any abstract concept….” (Applicant’s response 16). Additionally, Applicant asserts “… the Applicant is the first to discover that a machine-learning system integrating data synthesis, output-head probability prediction, portfolio determinization, and adaptive model re-training as recited in the claim can produce an improved machine-learning system that generates and executes asset portfolios….the technical elements and limitations recited in the claims are not generic but are positively tied and contribute to the solution…does not reflect a generic computer component, but a specific machine-learning system that the inventors have discovered to yield beneficial results when used in the rest of the claimed process.” (Applicant’s response 16-17).
Examiner respectfully disagrees as noted previously and in the rejection below. Applicant’s own arguments state that the invention is directed to “…dynamically training a machine learning model for asset portfolio generation and trade execution…” for “managed portfolios” and “for financial decision-making”; this is a method of organizing human activity [ fundamental economic concept and organizing human activity (commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)] (Applicant’s arguments 1-5). As recited the claimed limitations use generic computing elements to execute an abstract idea which are recited at a high level of generality. At most this is an improvement to the abstract idea (i.e. “asset portfolio generation and trade execution”). This is cited in the rejection below and previously, the technological elements are recited at a high level of generality. (See MPEP 2106.05 (d), (f) and (g)): (Specification, [129-131] computing devices may be server computers, workstations, personal computers, portable computer systems, handheld devices, mobile computing devise, wearable devices, body-mounted or implantable devices, smartphones, smart appliances, internetworking devices, autonomous or semi-autonomous devices such as robots or unmanned ground or aerial vehicles, any other electronic device that incorporates hard-wired and/or program logic to implement the described techniques, one or more virtual computing machines or instances in a data center, and/or a network of server computers and/or personal computers…hardware, software or combination of hardware and software… [132-149] computers….computer system…instructions… an Internet of Things…device… mobile computing device… storage media…. network link… execution of instructions… .) There is no improvement to technology. Further the citation of the Specification noted above, below and previously is Berkheimer evidence noting the use of generic computing elements for their well known, routine and conventional purpose (See MPEP 2106.05 (d)). The rejection under 35 USC 101 is not that the recited invention is well-known, routine and conventional. Applicant’s argument asserting a lack of Berkheimer evidence seems to conflates the novelty of the invention with patent eligibility. An improved or novel abstract idea is still an abstract idea. (Applicant argument 5) As such Applicant’s arguments are not persuasive.
Examiner reinstates the prior art rejection in view of Applicant’s amendments.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8-19, and 21-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. ____ (2014).
The claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In the instant case, the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
(1) In the instant case, the claims are directed towards a method and non-transitory computer readable medium using continuous machine learning model optimization to generate artificial intelligence managed portfolios. In the instant case, Claims 1-13 are directed to a process. Claims 14-19 and 21-26 are directed to a non-transitory computer readable medium.
(2a) Prong 1: Insert concept of invention. For example: Exchanging of securities (i.e. contracts) is categorized in/akin to the abstract idea subject matter grouping of: methods of organizing human activity [ fundamental economic concept and organizing human activity (commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)]. As such, the claims include an abstract idea.
The specific limitations of the invention are (a) identified to encompass the abstract idea include:
1. A …method executed … and comprising:
creating a first set of training data comprising synthesized market data that mimics characteristics of real market data, benchmarked to a time period, the synthesized market data having different distributions from the real market data;
… a … in a first stage using the first set of training data to produce … capable of outputting a recommendation of a particular asset portfolio;
determining an asset portfolio by inputting a particular account specification into the trained…comprising:
accessing, …, a plurality of candidate assets;
predicting, …l based on a respective candidate asset's factor value, a plurality of probabilities associated with the plurality of candidate assets being included in the asset portfolio; and
determining the asset portfolio based on the probabilities associated with the plurality of candidate assets being included in the asset portfolio;
determining a performance metric for the asset portfolio based on synthetic data and historic data associated with a plurality of assets;
determining a reference performance metric based on a reference asset portfolio and the synthetic and historic data;
comparing the performance metric for the asset portfolio with the reference performance metric to result in a decision of whether to … the …;
responsive to the decision to re-train the machine-learning model, creating a second set of training data comprising real market data derived from changes in real market conditions; and
… the … in a second stage using the second set of training data ; and
automatically executing one or more trades for an updated asset portfolio determined by the re-trained…, the executing comprising forming an order and … a programmatic call associated with the order to …r configured for trading.
14……… one or more sequences of instructions which, when executed …, cause the …to execute:
creating a first set of training data comprising synthesized market data that mimics characteristics of real market data, benchmarked to a time period, the synthesized market data having different distributions from the real market data;
……in a first stage using the first set of training data to produce a trained …capable of outputting a recommendation of a particular asset portfolio;
determining an asset portfolio by inputting a particular account specification into the … comprising:
accessing, …, a plurality of candidate assets;
predicting, …based on a respective candidate asset's factor value, a plurality of probabilities associated with the plurality of candidate assets being included in the asset portfolio; and
determining the asset portfolio based on the probabilities associated with the plurality of candidate assets being included in the asset portfolio;
determining a performance metric for the asset portfolio based on synthetic data and historic data associated with a plurality of assets;
determining a reference performance metric based on a reference asset portfolio and the synthetic and historic data;
comparing the performance metric for the asset portfolio with the reference performance metric to result in a decision of whether to re-train the …;
responsive to the decision to re-train the machine-learning model, creating a second set of training data comprising real market data derived from changes in real market conditions; and
… the… in a second stage using the second set of training data;
automatically executing one or more trades for an updated asset portfolio determined by the re-trained …the executing comprising forming an order and … a programmatic call associated with the order … configured for trading.
As stated above, this abstract idea falls into the (b) subject matter grouping of: methods of organizing human activity.
Prong 2: When considered individually and in combination, the instant claims are do not integrate the exception into a practical application because the steps of creating….training…, determining…by inputting…accessing… predicting… determining…, determining…, determining… , comparing… , re-training…, … executing… trades…, repeating the… re-training…. do not apply, rely on, or use the judicial exception in a manner that that imposes a meaningful limitation on the judicial exception (i.e. the abstract idea).
The instant recited claims including additional elements (i.e. … storing…transmitting…) do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The limitations merely recite: “apply it” (or an equivalent) or merely include instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception or (See MPEP 2106.05 (f) and (g))
(2b) In the instant case, Claims 1-6, 9-13 are directed to a process. Claims 14-19, and 21-26 are directed to a non-transitory computer readable medium.
Additionally, the claims (independent and dependent) do not include additional elements that individually or in combination are sufficient to amount to significantly more than the judicial exception of abstract idea (i.e. provide an inventive concept). As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: (train…a machine learning model…; re-train….machine learning model… computer…non-transitory computer-readable storage medium….processors… API) merely uses a computer as a tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception or merely uses generic computing elements to perform well known, routine, and conventional functions. (See MPEP 2106.05 (d), (f) and (g)): (Specification, [75] communication mechanisms (e.g. APIs…)[129-131] computing devices may be server computers, workstations, personal computers, portable computer systems, handheld devices, mobile computing devise, wearable devices, body-mounted or implantable devices, smartphones, smart appliances, internetworking devices, autonomous or semi-autonomous devices such as robots or unmanned ground or aerial vehicles, any other electronic device that incorporates hard-wired and/or program logic to implement the described techniques, one or more virtual computing machines or instances in a data center, and/or a network of server computers and/or personal computers…hardware, software or combination of hardware and software… [132-149] computers….computer system…instructions… an Internet of Things…device… mobile computing device… storage media…. network link… execution of instructions… .)
The dependent claims have also been examined and do not correct the deficiencies of the independent claims.
It is noted that claim (2-13 and 15-26) introduces the additional elements of wherein clauses further defining claim elements …first set of training data… account specification… (Claims 2,3,15, 16,)…asset portfolio… (Claims 4, 17 )…synthetic data, determining.. selecting…(Claims 5 and 18); …… performance metric… (Claims 8, 21) and additional steps to further define the invention: …re-training…. (Claims 9 and 22)… …comparing….selecting…(Claims 10 and 23)… determining…(Claims 11 and 24)…determining… comparing…(Claims 12 and 25); determining… determining… comparing… (Claims 13 and 26); generating… determining… (Claims 6 and 19). This element is not a practical application of the judicial exception because the limitations merely recite: “apply it” (or an equivalent) or merely include instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception (See MPEP 2106.05 (f) and (g)) Further these limitations taken alone or in combination with the abstract do not amount to significantly more than the abstract idea alone because these element(s) amount to mere use of a computer as a tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception or merely uses generic computing elements to perform well known, routine, and conventional functions. (See MPEP 2106.05 (d), (f) and (g)) (Specification, [129-131] computing devices may be server computers, workstations, personal computers, portable computer systems, handheld devices, mobile computing devise, wearable devices, body-mounted or implantable devices, smartphones, smart appliances, internetworking devices, autonomous or semi-autonomous devices such as robots or unmanned ground or aerial vehicles, any other electronic device that incorporates hard-wired and/or program logic to implement the described techniques, one or more virtual computing machines or instances in a data center, and/or a network of server computers and/or personal computers…hardware, software or combination of hardware and software… [132-149] computers….computer system…instructions… an Internet of Things…device… mobile computing device… storage media…. network link… execution of instructions… .)
Therefore, Claims 1-6, 8-19, and 21-26 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
Claims 1-6, 8-19, and 21-26 are rejected under 35 U.S.C. 103 as being unpatentable over US 12236485 B2, Gorantla et al. hereinafter referred to as Gorantla further in view of US 20190370308 A1, Riggs hereinafter referred to as Riggs.
Gorantla discloses one or more non-transitory computer-readable storage media and a computer-implemented method executed using a computer and comprising:
…
training a machine-learning model in a first stage using the first set of training data to produce a trained machine-learning model capable of outputting a recommendation of a particular asset portfolio; (See at least Gorantla, C10L13-30 … mechanism for providing portfolio deviation analytics … C11L25-39 , account data…C12L4-45…. Portfolio… resolution action for the portfolio … based on the corresponding deviation amount….C13L23-36 training models … such as machine learning model…)
determining an asset portfolio by inputting a particular account specification into the machine-learning model, (See at least Gorantla, C10L13-30 … mechanism for providing portfolio deviation analytics … C11L25-39 , account data…C12L5-45…. Portfolio… ) comprising:
accessing, by the machine-learning model, a plurality of candidate assets; (See at least Gorantla C12L4-45…. Portfolio…collection of assets…)
predicting, by a plurality of output heads of the machine-learning model based on a respective candidate asset's factor value, a plurality of probabilities associated with the plurality of candidate assets being included in the asset portfolio; and (See at least Gorantla C12L4-60…. Portfolio…collection of assets…deviation amount…predictive model that determines the resolution action… C13L15-C14L13 model include trading models… trained using new data… updated the corresponding trade data element…. )
determining the asset portfolio based on the probabilities associated with the plurality of candidate assets being included in the asset portfolio; (See at least Gorantla, C10L13-30 … mechanism for providing portfolio deviation analytics … C11L25-39 , account data…C12L5-45…. Portfolio…)
determining a performance metric for the asset portfolio based on … data and historic data associated with a plurality of assets; (See at least Gorantla, C12L5-45…. Portfolio…deviation amount may correspond to an amount by which the balance of a margin account exceed or falls below an initial margin… resolution action for the portfolio … based on the corresponding deviation amount…C12L57-65 historical pattern of a plurality of historical resolution actions….)
determining a reference performance metric based on a reference asset portfolio and the … and historic data; (See at least Gorantla, C11L25-39 , reference data… information relates to ..portfolio… composition information, margin information and exposure information…)
comparing the performance metric for the asset portfolio with the reference performance metric to result in a decision of whether to re-train the machine-learning model; (See at least Gorantla, C12L5-45 deviation represents change in position… C13L23-43 training models … such as machine learning model…actively utilized …while continuing to be trained using new data… )
responsive to the decision to re-train the machine-learning model, creating a second set of training data comprising real market data derived from changes in real market conditions; and (See at least Gorantla, C13L23-43 training models … such as machine learning model…actively utilized …while continuing to be trained using new data…).
re-training the machine-learning model in a second stage using the second set of training data; and (See at least Gorantla, C13L23-43 training models … such as machine learning model…actively utilized …while continuing to be trained using new data…).
automatically executing one or more trades for an updated asset portfolio determined by the re-trained machine learning model, the executing comprising forming an order and transmitting a programmatic call associated with the order to an application programming interface (API) of a server computer configured for trading. (See at least Gorantla, C12L35-56… resolution action may include a margin action call… payment request action… payment initiation…. A client request such as…. A cash deposit request, a securities deposit request… and a liquidated investments request… payment initiation broker action to transfer funds to the client… C13L44-55 resolution action .. automatically initiated… to nullify deviation amount… )
Gorantla does not directly disclose the following; however Riggs suggests:
creating a first set of training data comprising synthesized market data that mimics characteristics of real market data, benchmarked to a time period, the synthesized market data having different distributions from the real market data; (See at least Riggs [85-86] identifying and index or benchmark to track .. .track substantially or replicate the performance of the identified index or benchmark….[224-228] synthetic conglomerates….engineered composite…trackable.. benchmark… customized portfolio… [248] composite portfolios of the total market or a subset thereof…. )
…synthetic …data……(See at least Riggs, [86] synthetic conglomerate….[224-228] synthetic conglomerates….engineered composite…)
Furthermore, the Supreme Court has supported in KSR International Co. Teleflex Inc. (KSR), 550US___, 82 USPQ2d 1385 (2007), that merely applying a known technique to a known method, yield predictable results, render the claimed invention obvious over such combination. In the instant case, Gorantla discloses a method and system of providing deviation analytics to facilitate collateral management in portfolios including training models using data. Riggs teaches a method and system of using algorithms including the creation of synthetic conglomerate which suggests synthetic data being used. One of ordinary skill in the art would clearly recognize that this combination would lead to a predictable result (i.e. a method and system of portfolio analysis using a training model including synthetic data). As such the claimed invention is obvious over Gorantla / Riggs.
Claims 2 and 15
Gorantla and Riggs disclose the invention as claimed above in Claims 1 and 14.
Gorantla further discloses:
wherein each account specification comprises one or more of an objective, an uncertainty parameter, an account setting, a user preference, a candidate asset, model information associated with the machine-learning model, a reallocation frequency, a rebalancing frequency, or a tax harvesting goal. (See at least Gorantla, C8L50-L57 … data that relates to trade data, transaction information, valuations, reference data, portfolio data, deviation amounts, resolution actions, margin requirements summaries, and margin action calls…. C11L25-67, account …data…reference data…portfolio…)
Claims 3 and 16
Gorantla and Riggs disclose the invention as claimed above in Claims 1 and 14.
Gorantla further discloses:
wherein each account specification is associated with a time. (See at least Gorantla, C8L50-L57 … data that relates to trade data, transaction information, valuations, reference data, portfolio data, deviation amounts, resolution actions, margin requirements summaries, and margin action calls…. C11L25-67, account …data…reference data…portfolio…C12L4-19, investment position…net holding at a particular time….)
Claims 4 and 17
Gorantla and Riggs disclose the invention as claimed above in Claims 1 and 14.
Gorantla further discloses:
wherein the asset portfolio comprises one or more assets, each of the assets being associated with one or more of a weight value, a monetary value, an asset type, an asset identifier, a factor, or a factor value. (See at least Gorantla, C8L50-L57 … data that relates to trade data, transaction information, valuations, reference data, portfolio data, deviation amounts, resolution actions, margin requirements summaries, and margin action calls…. C11L25-67, account …data…reference data…portfolio…C12L4-19, financial investments… investment position…net holding at a particular time….)
Claims 5 and 18
Gorantla and Riggs disclose the invention as claimed above in Claims 4 and 14.
Gorantla further discloses:
determining the plurality of candidate assets based on one or more objectives and one or more criteria; and (See at least Gorantla, C12L4-19, financial investments… investment position……net holding at a particular time….parameter may relate to a characteristic)
Gorantla does not directly disclose the following; however Riggs suggests:
selecting the one or more assets from the plurality of candidate assets.
(See at least Riggs, [66] …composite portfolio… can be formed by selecting a group of investment securities…)
Claims 6 and 19
Gorantla and Riggs disclose the invention as claimed above in Claims 1 and 14.
Gorantla further discloses:
generating one or more factors based on the ….and historic data; and (See at least Gorantla, C8L50-L57 … data that relates to trade data, transaction information, valuations, reference data, portfolio data, deviation amounts, resolution actions, margin requirements summaries, and margin action calls…. C10L31-36 trade data and reference data repository.. margin requirements… portfolio deviation C11L25-67, account …data…reference data…portfolio…C12L32-45, deviation amount exceeds a predetermined threshold…current risk exposure as well as predicted risk exposure…)
determining the performance metric for the asset portfolio and the reference performance metric based on the one or more factors. (See at least Gorantla, C8L50-L57 … data that relates to trade data, transaction information, valuations, reference data, portfolio data, deviation amounts, resolution actions, margin requirements summaries, and margin action calls…. C10L31-36 trade data and reference data repository.. margin requirements… portfolio deviation C11L25-67, account …data…reference data…portfolio…C12L32-45, deviation amount exceeds a predetermined threshold…current risk exposure as well as predicted risk exposure…)
Gorantla does not directly disclose the following; however Riggs suggests:
…synthetic …data… ( See at least Riggs, [86] synthetic conglomerate….[224-228] synthetic conglomerates….engineered composite…)
Claims 8 and 21
Gorantla and Riggs disclose the invention as claimed above in Claims 1 and 14.
Gorantla does not directly disclose the following; however Riggs suggests:
wherein the performance metric comprises one or more of Sharpe ratio, Sortino ratio, Treynor ratio, Calmar ratio, Martin ratio, upside potential ratio, information ratio, or absolute return. ( See at least Riggs [75] performance… portfolio… Sharpe Ratio… Sortino ratio… )
Claims 9 and 22
Gorantla and Riggs disclose the invention as claimed above in Claims 1 and 14.
Gorantla further discloses:
comparing the performance metric for the asset portfolio with the reference performance metric to result in a decision to re-train the machine-learning model based on the reference performance exceeding the performance metric for the asset portfolio. (See at least Gorantla, C12L5-45 deviation represents change in position…C13L23-43 training models … such as machine learning model…actively utilized …while continuing to be trained using new data….C14L1-13… reference data may be determined to be stale when the age of the reference data exceeds the associated predetermined time threshold…reference data element updated when stale…)
Claims 10 and 23
Gorantla and Riggs disclose the invention as claimed above in Claims 1 and 14.
Gorantla further discloses:
comparing the performance metric for the asset portfolio with the reference performance metric based on the performance metric for the asset portfolio exceeding the reference performance metric to result in a decision to not re-train the machine-learning model; and (See at least Gorantla, C12L5-45 deviation represents change in position… C13L23-43 training models … such as machine learning model…actively utilized …while continuing to be trained using new data….C14L1-13… reference data may be determined to be stale when the age of the reference data exceeds the associated predetermined time threshold…reference data element updated when stale…)
selecting the asset portfolio for executing the one or more trades. (See at least Gorantla, C12L35-56… resolution action may include a margin action call… payment request action… payment initiation…. A client request such as…. A cash deposit request, a securities deposit request… and a liquidated investments request… payment initiation broker action to transfer funds to the client… C13L44-55 resolution action .. automatically initiated… to nullify deviation amount…wherein the selection of the portfolio occurs when the resolution action is taken )
Claims 11 and 24
Gorantla and Riggs disclose the invention as claimed above in Claims 1 and 14.
Gorantla further discloses:
determining the specified frequency or timing based on the particular account specification. (See at least Gorantla, C13L64-C15L5 reference data … predetermined time threshold…)
Claims 12 and 25
Gorantla and Riggs disclose the invention as claimed above in Claims 1 and 14.
Gorantla further discloses:
determining one or more objectives associated with the particular account specification; and (See at least Gorantla, C14L2-14… trade data … reference data…update…when stale…feedback loop … ensure data fidelity…)
comparing the asset portfolio with the reference asset portfolio based on the objectives to result in a decision of whether to re-train the machine-learning model. (See at least Gorantla, C13L23-L35, … training models…C14L2-14… trade data … reference data…update…when stale…feedback loop … ensure data fidelity…Claim 1…. Claim 7…updated based on corresponding information that relates to the at least on resolution action)
Claims 13 and 26
Gorantla and Riggs disclose the invention as claimed above in Claims 1 and 14.
Gorantla further discloses:
determining a performance distribution associated with the asset portfolio; (See at least Gorantla, C8L50-L57 … data that relates to trade data, transaction information, valuations, reference data, portfolio data, deviation amounts, resolution actions, margin requirements summaries, and margin action calls…11L25-39 , reference data… information relates to ..portfolio… composition information, margin information and exposure information… C12L5-45 deviation represents change in position…. C13L23-L35, … training models…C14L2-14… trade data … reference data…update…when stale…feedback loop … ensure data fidelity…Claim 1…. Claim 7…updated based on corresponding information that relates to the at least on resolution action)
determining a reference performance distribution associated with the reference asset portfolio; and (See at least Gorantla, C13L23-L35, … training models…C14L2-14… trade data … reference data…update…when stale…feedback loop … ensure data fidelity…Claim 1…. Claim 7…updated based on corresponding information that relates to the at least on resolution action)
comparing the performance distribution associated with the asset portfolio with the reference performance distribution to result in a decision of whether to re-train the machine-learning model. (See at least Gorantla, C13L23-L35, … training models… C13L23-43 training models … such as machine learning model…actively utilized …while continuing to be trained using new data….C14L2-14… trade data … reference data…update…when stale…feedback loop … ensure data fidelity…Claim 1…. Claim 7…updated based on corresponding information that relates to the at least on resolution action)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jaeger, M. et al. “Interpretable Machine Learning for Diversified Portfolio Construction” The Journal of Financial Data Science, Summer 2021, 3 (3) 31-51. (https://www.pm-research.com/content/iijjfds/3/3/31.full.pdf)
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/ASHA PUTTAIA H/Primary Examiner, Art Unit 3691
