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 1-20 are pending.
Claims 1, 6, 8, 11, 14 and 17 were amended and/or newly added in the Applicant’s filing(s) on 7/14/2026.
This office action is being issued in response to the Applicant's filing(s) on 7/14/2026.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 8/14/2026 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 alternative 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. The Applicant should review all claims for additional claim interpretation issues.
Claim 1 recites a method comprising:
applying, by the processor set, the trained machine learning model having the probabilistic framework to improve an accuracy of prediction of the future return of the asset for the future time period indicated by the received user input model.
Method claims are defined by the method steps being actively performed, not the motivation for performance of the method steps (i.e., to improve the accuracy of prediction).
Claims 8 and 14, due to similar claim language, result in a similar claim interpretation.
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 1-20 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 from … a user …, the user input indicating 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;
creating, …, a training data set based on user input, a training data set based on the received input, including time series data of a price of an asset and stochastic event data of events related to the asset indicated by the received user input;
… a model having a probabilistic framework for modeling event intensity, event magnitude, and their effects on a probabilistic time series of a return of the asset, wherein the … model includes:
… an event intensity model that models an event intensity parameter of one of the events related to the asset, wherein the event intensity model comprises a proximal graphical event model (PGEM) that captures dependencies of exogenous future events related to the asset …; and
… a probabilistic time series model that incorporates event impact by adjusting a mean of the probabilistic time series and predicts a probability distribution of the return of the asset …
applying, …, a … model having the probabilistic framework to improve an accuracy of a prediction of the return of the asset for the future time indicated by the received user input.
These limitations, as drafted, under its broadest reasonable interpretation, cover 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 the specification recites that the method results in “optimizing a portfolio based on the estimated asset risk.” (see para. 121).
Additionally, the 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. (2014).
These limitations, as drafted, under their broadest interpretation, also 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).
The claim(s) also 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:
training, by the processor set, a probabilistic time series model that incorporates event impact by adjusting a mean of the probabilistic time series …
The limitations, as drafted, recite a mathematical concept (e.g., mathematical relationships, mathematical formulas or equations, and mathematical calculations) which is an enumerated grouping of abstract ideas. See MPEP §2106.04(a)(2)(I).
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 14), graphical user interface (Claim(s) 1, 8 and 14), a user device (Claim(s) 1, 8 and 14), machine learning (Claim(s) 1, 8 and 14), and computer readable storage media (Claim(s) 8 and 14).
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, 9-13 and 15-20 recite claim limitations that further define the abstract idea recited in respective independent Claim(s) 1, 8 and 14. As such, the dependent claims are also grouped as 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 are 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.
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) 1-6 and 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dembo (US PG Pub. 2019/0197206) in view of Gao (Bhattacharjya, Debarun; Sumbramanian, Dharmashankar; Gao, Tian. Proximal Graphical Event Models. 32nd Conference on Neural Information Processing Systems. 2018. Montreal, Canada) and Karni (US PG Pub. 2023/0121239).
Regarding Claim 1, Dembo discloses a method comprising:
receiving, by a processor set, user input from a graphic user interface of a user device. (see para. 7 and 137);
the user input indicating an asset (e.g., Australian Dollar) and events related to the asset (e.g., a drought and high temperatures). (see para. 199);
creating, by the processor set, a training data set based on the received user input (expert input or historical data), the training data set including time series data of a price of the asset (bond values or historical returns) and stochastic event data of the events related to the asset indicated by the user input (expert input data). (see para. 116-117, 147, 169, 189, 199, 206 and 333);
training, by the processor set, based on the received user input (expert input), a machine learning model having a probabilistic framework for modeling event intensity, event magnitude, and their effects on a probabilistic time series of a return of the asset, wherein the training of the machine learning model includes:
training (training or generating), by the processor set, an event intensity model that models an event intensity (impact) parameter of one of the events (underlying event) related to the asset (e.g., asset value), wherein the event intensity model comprises an event model that capture dependencies (dependencies and correlations) of exogenous future events (macro factors or events) related to the asset, and wherein the creating the event intensity model comprises learning parameters of the event model using machine learning on the training data set. (see fig. 6A-7C; para. 113-116, 120, 135 and 195-203);
training (training or generating), by the processor set, a probabilistic time series model that incorporates event intensity (impact) and predicts a probability distribution of a return (loss or gain) of the asset (portfolio), wherein the training of the probabilistic time series model comprises learning parameters of the probabilistic time series model using machine learning and the training data set. (see fig. 11-17; para. 273-280); and
applying, by the processor set, the trained learning model having the probabilistic framework to improve an accuracy of the prediction of the future return (loss or gain) of the asset (portfolio) for the future time period (time horizon). (see fig. 11-17; para. 273-280).
Dembo does not explicitly teach a method wherein the user input received is indicating a target parameter for optimizing the asset and a future time period for forecasting a future return of the asset.
However, Dembo discloses a method comprising a processor set (computer) utilizing data comprising data indicating a target parameter (particular metric) for optimizing the asset and a future time period (time horizon) for forecasting a future return of the asset (see fig. 3A; para. 111-113, 144, 161, 206-208 and 239).
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 receive the data utilized by Dembo via user input through a graphical user interface, as disclosed by Dembo, as a graphic user interface is a standard and conventional means by which a computer system receives user input.
Dembo does not teach a method wherein the model that captures dependencies of exogenous future events related to the asset is a proximal graphical event model (PGEM).
Gao discloses a method wherein the event model is a proximal graphical event model (PGEM) that captures dependencies of exogenous future events related to the asset. (see abstract).
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 by incorporating a PGEM, as disclosed by Gao, as “PGEMs are particularly interpretable event models and could be useful for providing insights about the dynamics in an event dataset to … financial analysts.” (See Gao, p. 2).
Dembo does not teach a method comprising adjusting the mean of the probabilistic time series.
Karni discloses a method comprising adjusting the mean of the probabilistic time series (via weighted exponential moving average). (see para. 6).
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 and Gao by incorporating an adjustment to the mean of the time series, as disclosed by Karni, as assigning higher weights to more recent events would make the model more responsive to recent events.
Regarding Claim 2, Dembo discloses a method comprising configuring wherein the probabilistic time series model such that the predicted probability distribution of the return of the asset at a specific time comprises a normal distribution having a constant variance and a mean. (see fig. 11-17).
Dembo does not teach a method wherein the mean is adjusted based on the stochastic event data.
Karni discloses a method wherein the mean is adjusted (via weighted exponential moving average) based on the stochastic event data. (see para. 6).
It would have been obvious to one of ordinary skill in the art before the before the effective filing date of the invention to have modified Dembo and Gao by incorporating an adjustment to the mean of the time series, as disclosed by Karni, as assigning higher weights to more recent events would make the model more responsive to recent events.
Regarding Claim 3, Dembo does not explicitly teach a method wherein the event intensity model is based on a time window of the stochastic event data that is less than all the stochastic event data.
However, in the case where the claimed ranges (i.e., less than all the stochastic event data) “overlap or lie inside ranges disclosed by the prior art” (i.e., all the stochastic event data) a prima facie case of obviousness exists. See §2144.05(I), citing In re Wertheim, 191 USPQ 90 (CCPA 1976).
Regarding Claim 4, Dembo discloses a method creating a causal relationship graph (tree of interrelationships between factors) using the event intensity model. (see fig. 9; para. 133-135).
Regarding Claim 5, Dembo discloses a method comprising creating an event magnitude model that models a distribution of magnitude of the events related to the asset based on previous magnitudes of the events related to the asset (either via feedback or past events). (see fig. 5B-5D; para. 140, 182 and 230-236).
Regarding Claim 6, Dembo discloses a method wherein:
the asset (e.g., equities) is on of plural assets (e.g., a portfolio having equities, fixed income products and derivative products). (see para. 144); and
comprising training a respective event intensity model and a respective probabilistic time series model for each of the plural asset (each particular portfolio/asset). (see para. 144); and
executing a portfolio optimization (electronic transactions such as buy/sell, hedge, un-hedge, cancel and modify) for a portfolio including the plural assets using respective predicted future returns of the plural assets. (see para. 144).
Regarding Claim 14, such claim recites substantially similar limitations as claimed in previously rejected claims (Claim 8) and, therefore, would have been obvious based upon previously rejected claims (Claim 8).
Claim 14 additionally recites a system wherein the event intensity model predicts a probability density of the one or events happening at a particular time.
Dembo discloses wherein the event intensity model predicts a probability density of the one or more events (moves) happening at a particular time (date range). (see para. 333).
Regarding Claim 15-19, such claim recites substantially similar limitations as claimed in previously rejected claims (Claims 2-6) and, therefore, would have been obvious based upon previously rejected claims (Claims 2-6).
Claim(s) 7-13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dembo and Gao, as applied to Claims 1 and 14 above, and further in view of Saulys (US Patent 11,526,524).
Regarding Claim 7, Dembo discloses a method wherein the stochastic event data comprises consensus adjustment data (weights applied to expert inputs) related to the asset. (see para. 229).
Dembo does not teach a method wherein the stochastic event data comprises revenue release data related to the asset.
Saulys discloses a method wherein the stochastic event data comprises revenue release data (earnings reports) related to the asset. (see col. 3, lines 55-63).
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, Gao and Karni by incorporating revenue release data, as disclosed by Saulys, as revenue release data is an important data type “used for time series analysis in the financial field.” (see Saulys, col. 3, lines 55-63).
Regarding Claim 8, such claim recites substantially similar limitations as claimed in previously rejected claims (Claim 1) and, therefore, would have been obvious based upon the previously rejected claim (Claim 1).
Claim 8 additionally recites a computer product wherein the stochastic event data comprises: revenue release data that defines revenue of a company associated with the asset; and consensus adjustment data that defines revenue estimation of the company aggregated from plural entities other than the company.
Dembo discloses wherein the stochastic event data comprises consensus adjustment data (weights applied to expert inputs) aggregated from plural entities other than the company. (see para. 229).
Dembo does not teach a method wherein the stochastic event data comprises revenue release data related to the asset.
Saulys discloses a method wherein the stochastic event data comprises revenue release data (earnings reports) related to the asset. (see col. 3, lines 55-63).
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, Gao and Karni by incorporating revenue release data, as disclosed by Saulys, as revenue release data is an important data type “used for time series analysis in the financial field.” (see Saulys, col. 3, lines 55-63).
Regarding Claims 9-13, such claim recites substantially similar limitations as claimed in previously rejected claims (Claims 2-7) and, therefore, would have been obvious based upon previously rejected claims (Claims 2-7).
Regarding Claim 20, Dembo discloses a system wherein:
the asset comprises a stock (equity) associated with a company. (see para. 144);
the time series data of the price of the asset comprises historical stock prices of the stock (equity). (see para. 144 and 239);
the stochastic event data comprises: consensus adjustment data (weights applied to expert inputs) related to the asset. (see para. 229); and
the consensus adjustment data aggregated from plural entities other than the company. (see para. 229).
Dembo does not teach a system wherein the time series data of the price of the asset comprises historic daily stock prices of the stock; the stochastic event data comprises revenue release data related to the asset; the revenue release data defines revenue of the company reported by the company; and the consensus adjustment data defines revenue estimation of the company aggregated from plural entities other than the company.
Saulys discloses a system wherein:
the asset comprises a stock associated with a company. (see col. 16, lines 35-46);
the time series data of the price of the asset comprises historical daily stock prices of the stock. (see col. 16, lines 35-46);
the stochastic event data comprises revenue release data (earnings reports) related to the asset. (see col. 3, lines 55-63);
the revenue release data defines revenue (earnings) of the company reported by the company. (see col 3, lines 55-63); and
the data defines revenue estimation (earnings) of the company. (see col 3, lines 55-63).
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, Gao and Karni by incorporating revenue release data, as disclosed by Saulys, as revenue release data is an important data type “used for time series analysis in the financial field.” (see Saulys, col. 3, lines 55-63).
7. Response to Arguments
Applicant's arguments filed 7/14/2026 were addressed in the Advisory Action issued on 7/30/2026. No new arguments were submitted in the Applicant’s filing on 8/14/2026.
The prior art rejection has been rewritten and/or remapped to account for the amended claim language submitted on 7/14/2026.
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 September 11, 2026
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