DETAILED ACTION
1. 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 .
2. Status of Application and Claims
Claims 1-13 are pending.
Claims 1-4 and 8-11 were amended or newly added in the Applicant’s filing(s) on 3/27/2026.
This office action is being issued in response to the Applicant's filing(s) on 3/27/2026 and 5/04/2026.
3. Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/04/2026 has been entered.
4. Claim Interpretation
The subject matter of a properly construed claim is defined by the terms that limit its scope when given their broadest reasonable interpretation. see MPEP §2013(I)(C). Specifically, the “broadest reasonable construction ‘in light of the specification as it would be interpreted by one of ordinary skill in the art.’” See MPEP §2111, citing Phillips v. AWH Corp., 75 USPQ2d 1321, 1329 (Fed. Cir. 2005). However, “[t]hough understanding the claim language may be aided by explanations contained in the written description, it is important not to import into claim limitations that are not part of the claim.” See MPEP §2111.01, citing Superguide Corp. v. DirecTV Enterprises, Inc., 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). Construing claims broadly during prosecution is not unfair to the applicant, because the applicant has the opportunity to amend the claims to obtain more precise claim coverage. See MPEP §2111, citing In re Yamamoto, 222 USPQ 934, 936 (Fed. Cir. 1984).
As a general matter, grammar and the plain meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. See MPEP §2013(I)(C). Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. See MPEP §2013(I)(C).
As such, claim limitations that contain statement(s) such as “if,” “may,” “might,” “can,” and “could” are treated as containing optional language. See MPEP §2013(I)(C). As matter of linguistic precision, optional claim elements do not narrow claim limitations, since they can always be omitted. See MPEP §2013(I)(C).
Similarly, a method step exercised or triggered upon the satisfaction of a condition, where there remains the possibility that the condition was not satisfied under the broadest reasonable interpretation, is an optional claim limitation. See MPEP §2111.04(II). As the Applicant does not address what happens should the optional claim limitations fail, Examiner assumes that nothing happens (i.e., the method stops). An alternate interpretation is that merely the claim limitations based upon the condition are not triggered or performed.
In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See MPEP §2143.03, citing Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298 (Fed. Cir. 2009);
Language in a method or system claim that states only the intended use or intended result, but does not result in a manipulative difference in the steps of the method claim nor a structural difference between the system claim and the prior art, fails to distinguish the claims from the prior art.
The following types of claim language may raise a question as to its limiting effect (this list is not exhaustive):
Statements of intended use or field of use, including statements of purpose or intended use in the preamble. See MPEP §2111.02;
Clauses such as “adapted to”, “adapted for”, “wherein”, and “whereby.” See MPEP §2111.04;
Contingent limitations. See MPEP §2111.04(II);
Printed matter. See MPEP §2111.05; and
Functional language associated with a claim term. See MPEP §2181.
As such, while all claim limitations have been considered and all words in the claims have been considered in judging the patentability of the claimed invention, the following italicized, underlined and/or boldened language is interpreted as not further limiting the scope of the claimed invention.
Additionally, the following italicized, underlined and emboldened language is not necessarily an exhaustive list of claim language that is interpreted as not further limiting the scope of the claimed invention. Applicant should review all claims for additional claim interpretation issues.
Claim 1 recites a method comprising:
creating, by the processor set, a probabilistic time series model for estimating a dynamic covariance matrix based on impacts of the events related to the asset, wherein creating the probabilistic time series model comprises:
Method claims are defined by the method steps being actively performed (i.e., creating a probabilistic time series model), not the intended purpose or the motivation for the performance of the method steps (i.e., the probabilistic time series model is for estimating a dynamic covariance matrix) nor method steps possibly performed in the future (i.e., estimating a dynamic covariance matrix).
Examiner also notes that the intended purpose of the probabilistic times series model is for estimating a dynamic covariance matrix, and the intended purpose of the dynamic covariance matrix, in turn, is to account for impacts of the events related to the asset.
Claim 8, due to similar claim language, results in a similar claim interpretation.
Claim 1 recites a method comprising:
transmitting, by the processor set, the predicted future return to display on the graphical user interface.
Method claims are defined by the method steps being actively performed (i.e., transmitting the predicted future return), not the intended purpose or the motivation for the performance of the method steps (i.e., to display on the graphical user interface) nor method steps possibly performed in the future (i.e., a potential future display on the graphical user interface).
5. 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 1-13 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1
The claimed invention falls within one of the four statutory categories of invention (i.e., process, machine, manufacture and composition of matter). See MPEP §2106.03.
STEP 2A – PRONG ONE
The claim(s) recite(s) a method, a system to perform a method and/or computer-readable medium containing instructions, when executed, causes a computer to perform a method comprising:
receiving, … , user input …, the user input defining an asset, events related to the asset, a target parameter for optimizing the asset, and a future time period for forecasting a future return of the asset;
obtaining, …, stochastic event data of the events related to the asset …;
obtaining, …, time series data of a price of the asset from a data repository;
creating, … , a training data set using the received user input, the training data set including the time series data of the price of an asset and the stochastic event data of events related to the asset;
creating, … , a probabilistic framework for modeling event intensity, event magnitude, and their effects on a probabilistic time series of a return of the asset by:
creating, … , an event intensity model by using a multivariate Hawkes process for modeling an event intensity parameter of one of the events related to the asset, wherein creating the event intensity model comprises learning parameters of the event intensity model using … the training data set and the stochastic data in the training data set;
adjusting the event intensity model based on consensus updates associated with the events related to the asset;
generating, …, a casual relationship graph representing dependencies among the events related to the asset;
creating, … ,a probabilistic time series model for estimating a dynamic covariance matrix based on impacts of the events related to the asset, wherein creating the probabilistic time series model comprises:
determining the dependencies among the events from the from the casual relationship graph;
capturing the dependencies into the probabilistic time series model; and
learning parameters of the probabilistic time series model using … and the time series data of the price of the asset in the training data set; and
using the probabilistic time series model to predict a probability distribution of return of the asset;
predicting, … , a future return of the asset for the future time period using the probabilistic time series model, the predicting the future return of the asset comprises executing a plurality of simulations based on the probabilistic time series model; and
transmitting, … , the predicted future return …
These limitations, as drafted, under its broadest reasonable interpretation, covers a series of steps instructing how to predict the future return of an asset which is a fundamental economic practice, a sub-category of certain method(s) of organizing human activity, an enumerated grouping of abstract ideas. See MPEP §2106.04(a)(2)(II)(A).
Examiner notes that predicting the future return of an asset is mitigation of financial risk and that the mitigation of financial risk is a court-provided example of a fundamental economic practice. See MPEP §2106.04(a)(2)(II)(A), citing Alice Corp. v. CLS Bank, 573 U.S. 208, 218, 110 USPQ2d 1976, 1982 (2014).
Additionally, these limitations, as drafted, under its broadest interpretation, covers a series of steps that can be practically performed in the human mind (e.g., observations, evaluations, judgments and opinions) which are mental process, a second enumerated grouping of abstract ideas. See MPEP §2106.04(a)(2)(III).
Examiner notes that “’collecting information, analyzing it, and displaying certain results of the collection and analysis,’ where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind” is a mental process. See MPEP §2106.04(a)(2)(III)(A) citing Electric Power Group v. Alstom, SA. (Fed. Cir. 2016).
Accordingly, the claimed invention recites an abstract idea.
STEP 2A – PRONG TWO
The claimed invention recites additional elements (i.e., computer elements) of a processor set (Claim(s) 1 and 8), a graphical user interface of a user device (Claim(s) 1 and 8), machine learning (Claim(s) 1 and 8), and application programming interface(s) (APIs) (Claim(s) 1 and 8).
The claimed invention does not include additional elements that integrate the judicial exception into a practical application of the exception because the claims do not provide improvements to another technology or technical field; improvements to the functioning of the computer itself; are not applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; are not applying the judicial exception with or by use of a particular machine; are not effecting a transformation or reduction of a particular article to a different state or thing; and are not applying the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP §2106.04(d).
The additional elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. See MPEP §2106.05(f). Alternately, the additional elements amount to no more than generally linking the exception to a particular technological environment or field of use. See MPEP §2106.05(h). Accordingly, these additional element(s), when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claimed invention is directed to an abstract idea without a practical application.
STEP 2B
Upon reconsideration of the indicia noted under Step 2A in concert with the Step 2B considerations, the additional claim element(s) amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §2106.07(a)(II). The same analysis applies in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claim does not provide an inventive concept significantly more than the abstract idea.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
DEPENDENT CLAIMS
Dependent Claim(s) 2-7 and 9-13 recite claim limitations that further define the abstract idea recited in respective independent Claim(s) 1 and 8. As such, the dependent claims are also grouped an abstract idea utilizing the same rationale as previously asserted against the independent claims.
No additional computer components other than those found in the respective independent claims is recited, thus it is presumed that the claim is further utilizing the same generically recited computer.
As such, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application of the judicial exception or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
Accordingly, the dependent claim(s) are also not patent eligible.
Appropriate correction is requested.
6. Claim Rejections - 35 USC § 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dembo (US PG Pub. 2019/0197206) in view of Hawkes (Hawkes, Alan G. Hawkes Jump-Diffusions and Finance: A Brief History and Review. European Journal of Finance. April 23, 2020. pp. 1-17).
Regarding Claim 1, Dembo discloses a method comprising:
receiving, by a processor set, user input (expert input or input from data sources) from a graphical user interface of a user device, the user input defining an asset (historical data feeds), events related to the asset (historical data feeds), a target parameter (risk) for optimizing the asset, and a future time period (time horizon) for forecasting a future return of the asset. (see para. 129, 137, 195 and 206-207);
obtaining, by the processor set, stochastic event data of the events (historical data feeds for event detection) related to the asset. (see para. 129);
obtaining, by the processor set, time series data of a price of the asset (historical and current market sentiment, and price movements) from a data repository. (see para. 202 and 281);
creating, by a processor set, a training data set using received user input, the training data set including the time series data of a price of an asset (6 month of historical returns data, historical data, currency values, bond values, S&P index) and the stochastic event data of events (events) related to the asset. (see para. 116, 206, 217 and 332-333);
creating, by the processor set, based on the received user input a probabilistic framework for modeling event intensity, event magnitude, and their effects on a probabilistic time series of a return of the asset by:
creating, by the processor set, an event intensity model by using a multivariate process (taking into account multiple variables, factors and indicators) for modelling an event intensity parameter (impact score) of one of the events (macro factors) related to the assets. (see para. 113-114, 116, 120 and 135);
wherein the creating the event intensity model comprises learning parameters (interconnected factors and indicators) of the event intensity model using machine learning and the training data set. (see para. 113-114, 116, 120 and 135);
adjusting (updating) the event intensity model (data value probabilities) based on consensus updates (updated responses, new information) associated with the events related to the asset. (see para. 249, 260 and 313);
generating, by the processor set, a casual relationship graph representing dependencies among the events related to the asset. (see fig. 3B; para. 209-210);
creating, by the processor set, a probabilistic time series model for estimating a dynamic covariance matrix (covariance matrix) that accounts for impacts of the events related to the asset, wherein creating the probabilistic time series model comprises: (see para. 200);
determining the dependencies among the events from the causal relationship graph. (see fig. 3B; para. 209-210);
capturing the dependencies into the probabilistic time series model. (see fig. 57-59; para. 266 and 364-366);
learning parameters of the probabilistic time series model using machine learning and the time series data of the price of the asset in the training data set. (see fig. 57-59; para. 266 and 364-366);
using the probabilistic time series model to predict a probability distribution of a return of the asset. (see fig. 51 and 57-59; para. 358 and 364-366);
predicting, by the processor set, a future return of the asset (portfolio) for the future time period using the probabilistic time series model, the predicting the future return of the asset comprises executing a plurality of simulations based on the probabilistic time series. (see fig. 57-59; para. 133 and 364-366); and
transmitting, by the processor set, the predicted future return (valuation of portfolio) to display on the graphical user interface. (see para. 13).
Dembo does not explicitly teach a method wherein stochastic event data is obtained via an application programming interface (API), although Dembo does teach a method wherein data is communicated via an application programming interface (API). (see para. 158 and 198).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Dembo to utilize an API for communication of data, as suggested by Dembo, as an API is a standard and conventional computer component for communication of data.
Dembo does not teach a method wherein the multivariate process is a multivariate Hawkes process.
Hawkes discloses a method wherein the multivariate process is a multivariate Hawkes process. (see pp. 3-4).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to have modified Dembo by incorporating a multivariate Hawkes process, as disclosed by Hawkes, as Hawkes processes “have become popular in a very wide range of applications, including Finance.” See Hawkes, p. 4.
Regarding Claim 2, Dembo discloses a method comprising configuring the probabilistic time series model by using a process wherein the predicted probability distribution of the future return of the asset at a specific time comprises a normal distribution having a mean. (see fig. 57-59).
Examiner notes that all distributions inherently have a mean (i.e., a center value).
Dembo does not teach a method wherein the mean is based is a stochastic jump-diffusion process. (see pp. 3-6)
Hawkes discloses a method wherein the computation process is a stochastic jump-diffusion process. (see pp. 3-6).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to have modified Dembo and Hawkes by incorporating a stochastic jump-diffusion process, as disclosed by Hawkes, as stochastic jump-diffusion processes are used to “model various financial problems.” See Hawkes, p. 3.
Regarding Claim 3, Dembo discloses a method comprising adjusting (determining) the mean (average) of the normal distribution using the stochastic event data. (see fig. 51; para. 232, 234 and 236).
Dembo does not explicitly teach a method comprising adjusting a variance of the normal distribution using the stochastic event data, although a normal distribution inherently has a mean (i.e., the average of the data set) and a variance (i.e., the spread between numbers in a data set).
Hawkes discloses a method comprising adjusting the mean (stochastic mean) and a variance (stochastic volatility) of the normal distribution using the stochastic event data. (see p. 3). Examiner notes that volatility is a measure of the variance bounded by a specific time period. As such, measurement of volatility requires computation of variance.
It would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to have modified Dembo and Hawkes by incorporating a model including a mean and a variance, as disclosed by Hawkes, as the data set inherently possesses a mean and variance.
Regarding Claim 4, Dembo discloses a method comprising creating an event magnitude model (magnitude of impact or shocks) that models a distribution of magnitude of the events (shock distributions) related to the asset based on previous magnitudes of the events (feedback) related to the asset and adjusting the mean (average) of the distribution based on received consensus updates (updated responses and new information) associated with the events related to the asset. (see fig. 5B-5D; para. 113, 116, 140, 189, 197, 230-236, 249, 260 and 313);
Regarding Claim 5, Dembo discloses a method wherein the asset is one of plural assets (portfolio is a basket of different equities, fixed income products and derivatives products) (see para. 144), and the method further comprising:
creating a respective event intensity model and probabilistic time series model for each of the plural assets (particular asset). (see para. 144); and
executing a portfolio optimization (reducing risk or issuing transaction instructions) for a portfolio including the plural assets using respective predicted future returns of the plural assets. (see para. 144 and 275).
Regarding Claim 6, Dembo discloses a method wherein the executing the portfolio optimization comprises:
running simulations using the respective probabilistic time series models (see para. 123 and 255-257); and
determining a portfolio, based on the simulations, that minimizes portfolio volatility (reduces maximum loss) or maximizes Sharpe ratio. (see para. 275).
Regarding Claim 7, Dembo discloses a method wherein the stochastic event data comprises revenue release data (balance sheet items) related to the asset and consensus adjustment data (morph factors, which adjust expert inputs) related to the asset. (see para. 7 and 190).
Regarding Claim 8, such claim recites substantially similar limitations as claimed in previously rejected claims and, therefore, would have been obvious based upon previously rejected claims.
Claim 8 differs from previous claims by additionally reciting a claim limitation pertaining to the data elements comprising stochastic event data.
Dembo also discloses a computer program product wherein:
the stochastic event data comprises: revenue release data (balance sheet data) that defines revenue of a company (organization) associated with the asset (item). (see para. 7); and
consensus adjustment data (benchmarks) that defines revenue estimation of the company aggregated from plural entities (the market) other than the company. (see para. 274-275).
Regarding Claims 9-13, such claims recite substantially similar limitations as claimed in previously rejected claims and, therefore, would have been obvious based upon previously rejected claims.
7. Response to Arguments
Applicant's arguments filed 3/27/2026 have been fully considered but they are not persuasive.
§101 Rejection
Applicant’s arguments in the Applicant’s filing(s) on 3/27/2026 pertaining to the previously asserted §101 Rejection were addressed in the Advisory Action issued on 4/22/2026. No new arguments were submitted with the Request for Continued Examination on 5/26/2026.
§103 Rejection
Applicant argues that the previously asserted prior art (Dembo and Hawkes) fails to teach or suggest the newly amended claim limitations. See Arguments, pp. 14-16.
Specifically, Applicant argues:
The cited combination fails to teach or suggest the recited sequence of claimed steps for "creating, by the processor set, a probabilistic time series model for estimating a dynamic covariance matrix that accounts based on impacts of the events related to the asset", including:
(i) "capturing the dependencies into the probabilistic time series model," and
(ii) "learning parameters of the probabilistic time series model using machine learning and the time series data of the price of the asset in the training data set."
Dembo determines correlations or dependencies among macro factors (see, e.g., paragraph [0198] of Dembo). Further, Dembo discloses that macro factors may be independent or correlated and that conditional probabilities can be used to capture such correlations within the scenario tree (see, e.g., paragraph [0149] of Dembo).
However, Dembo does not teach or suggest capturing the dependencies into the probabilistic time series model for estimating a dynamic covariance matrix, as recited in the amended claim 1. See Arguments, p. 15 – emphasis original.
The Examiner respectfully disagrees.
Dembo recites:
A model generation platform is provided that generates or otherwise instantiates a model indicative of different scenarios for the events and outcomes. The model can indicate various upside and downside amplitudes associated with probabilistic determinations of impacts on various factors conditional on the occurrence of events and sub events, such as economic factors. The model may, for example, be in the form of a tree data structure, and this tree data structure may be traversed to perform various analyses or report generation. See para. 120 – emphasis added.
A scenario represents a path from a root node to a leaf node. This scenario can have a corresponding probability that can be generated or derived from the probabilities associated with the edges between all the nodes in the path of the tree that represents the particular scenario. Correlation or independence between the factors modelled by the tree can be used to derive the probability for the overall scenario or particular edge. Accordingly, scenario generation unit 124 models all possible scenarios for the event and outcomes along with probabilities for each of the scenarios to include not only the most likely scenarios but also outlier or rare scenarios that may still greatly impact the valuation of a portfolio. See para. 133 – emphasis added.
Derivation of micro-shocks is conditioned on move happening in macro variable by looking at time series historically. For deriving micro shocks in other currencies historical moves of greater than 5% in EUR can be looked at first. The 5% being derived from the poll. On the days EUR moved more than 5% the moves for GBP, JPY, and HKG can be extracted and the expected move in those currencies can be computed over the date range. For example, the value or shocks can move in EUR greater than 5% and shows the moves in GBP, HKD, JPY, and CHF on those same days. From this dataset we derive the shocks to be applied in other currencies. FIG. 41 shows an example chart of values. See para. 333 – emphasis added.
Dembo creates a probabilistic time series model (i.e. a statistical framework that describes a time series by modeling the conditional probability distribution of future values given past observations). See para. 120, 133 and 333.
Examiner notes that the claim, as written, recites that the claimed invention creates a probabilistic time series model for estimating a dynamic covariance matrix. The intended purpose of the probabilistic time series model is to estimate a dynamic covariance matrix. The claim, as written, does not estimate a dynamic covariance matrix utilizing the probabilistic time series model.
Regardless, Dembo recites:
The sequence of the macro factor nodes can be important. The probabilities can be conditional probabilities based on the preceding factor nodes in the tree or graph, for example. The system 100 can create a correlation matrix to generate probabilities. The matrix can have leafs and ends of trees as rows and the factors as the columns. The system 100 can use a variance and covariance matrix. The outcomes of each scenario can imply a correlation. If the variance is small then the factors can be correlated (e.g. if it is 0 then they are perfectly correlated). A given tree and poll can generate a covariance matrix. There may be multiple polls over time to generate multiple covariance matrices. The multiple covariance can matrices indicate changes over time (e.g. the variance of the variance). See para 200.
Dembo discloses estimating a dynamic covariance matrix based on impacts of the events related to the asset. See para. 200.
If Dembo “determines correlations or dependencies among macro factors,” then Dembo is determining and capturing the dependencies.
Applicant further argues:
Additionally, Dembo does not teach or suggest learning parameters of the probabilistic time series model using machine learning and the time series data of the price of the asset in the training data set. See Arguments, p. 15.
The Examiner respectfully disagrees.
Dembo recites:
Embodiments described herein relate to a fully automated scenario generation method. Events and outcomes or shocks provoke a need for understanding possible future scenarios. Armed with that information, system 100 uses machine learning techniques to gather information about the macro factors that can change significantly as a result of the event in question. For example, machine learning unit 120 can derive rules using data representing historical and current market sentiment and, using a model, develop a spanning set of scenarios or possible future states of the world. System 100 can estimate automatically the probabilities of these scenarios occurring, as influenced by the market view today and also with history that is relevant. See para. 202 – emphasis added.
Dembo discloses learning parameters of the probabilistic time series model using machine learning. (see para. 202).
Applicant further argues:
Further, none of the cited references teach or suggest the claimed feature "the predicting the future return of the asset comprises executing a plurality of simulations based on the probabilistic time series model,' as recited in amended claim 1. Dembo merely describes simulating victory conditions or outcomes (see, e.g., paragraph [0255] of Dembo). However, Dembo does not teach or suggest executing a plurality of simulations based on the probabilistic time series model, particularly in the context of a probabilistic time series model that captures dependencies among events (i.e., the claimed feature 'capturing the dependencies into the probabilistic time series model" as recited in amended claim 1). See Arguments, p. 15.
The Examiner respectfully disagrees.
Dembo recites:
A scenario represents a path from a root node to a leaf node. This scenario can have a corresponding probability that can be generated or derived from the probabilities associated with the edges between all the nodes in the path of the tree that represents the particular scenario. Correlation or independence between the factors modelled by the tree can be used to derive the probability for the overall scenario or particular edge. Accordingly, scenario generation unit 124 models all possible scenarios for the event and outcomes along with probabilities for each of the scenarios to include not only the most likely scenarios but also outlier or rare scenarios that may still greatly impact the valuation of a portfolio. See para. 133 – emphasis added.
The micro values can be utilized to estimate/track price movements of portfolios in view of various events taking place. For example, a portfolio manager holding JPY denominated assets may be interested in the potential price movement relative to the USD, and based on an analysis of outcomes of the event (e.g., an election), may decide to shift assets to more efficiently capture gains or to spread/limit maximum downside risk. For example, a portfolio manager may recognize that he/she will be exposing the portfolio to a large amount of downside risk and may choose to utilize a hedging strategy to offset the downside risk. See para. 239 – emphasis added.
Dembo discloses executing a plurality of simulations (scenarios) based on the probabilistic time series model. See para. 133.
Examiner notes that if Dembo is “simulating victory conditions or outcomes” then Dembo is executing a plurality of simulations.
If Dembo is being utilized to estimate/track movements of portfolios, then Dembo is predicting the future return of the asset (i.e., the portfolio). See para. 239.
8. Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON M. BORLINGHAUS whose telephone number is (571)272-6924. The examiner can normally be reached M-F 9-5.
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, RYAN D. DONLON can be reached on (571)270-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jason M. Borlinghaus/Primary Examiner, Art Unit 3692 June 12, 2026
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