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, 2, 5, 6, 8, 9, 11, 12, 14, 15, 17 and 18 were amended and/or newly added in the Applicant’s filing(s) on 12/18/2025.
This office action is being issued in response to the Applicant's filing(s) on 12/18/2025.
3. 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 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,
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; [and]
…
predicting, …, a future return of the asset for the future time period using the probabilistic time series model.
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 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 its 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 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.
4. 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);
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 events related to the asset. (see para. 116-117, 147, 169, 189, 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 by:
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 and 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
predicting, by the processor set, a future return (loss or gain) of the asset (portfolio) for the future time period (time horizon) using the probabilistic time series model. (see fig. 11-17; para. 273-280).
Dembo does not explicitly teach a method wherein the user input received is 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.
However, Dembo discloses a method comprising a processor set (computer) utilizing data comprising data defining an asset (types of assets, assets, or e.g., JPY), events related to the asset (e.g., macro factors, weather, world events, financial events, or an election), 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 time the invention was made 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 time the invention was made 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 time the invention was made 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 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).
5. Response to Arguments
Applicant's arguments filed 12/18/2025 have been fully considered but they are not persuasive.
§101 Rejection
Step 2A Prong One
Applicant argues that the claimed invention does not recite a judicial exception and, as such, satisfies Step 2A Prong One of the §101 Guidelines. See Arguments, pp. 10-12.
Specifically, Applicant argues:
These steps of training machine learning models having a probabilistic framework cannot be practically performed by a human mind, and therefore claim 1 does not recite a mental process. The USPTO memorandum titled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" dated August 4, 2025 ("August 2025 USPTO Memo") clearly reminds examiners that "[c]laim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within [the mental process] grouping". See August 2025 USPTO Memo at page 2. See Arguments, p. 10.
The Examiner respectfully disagrees, in part.
Based upon current §101 guidance, training a machine learning model is not a mental process, as training a machine learning model is not a process that can be practically performed in the human mind.
However, even if training a machine learning model is excluded from consideration, the remainder of the claimed invention still recites a mental process, a process that can be practically performed in the human mind (e.g., observations, evaluations, judgments and opinions).
The claimed invention still recites a method comprising:
receiving, by a processor set, user input from a graphical user interface of a user device, 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;
creating, by the processor set, a training data set based on the received user input, the training data set including time series data of a price of the asset and stochastic event data of events related to the asset;
…
predicting, by the processor set, a future return of the asset for the future time period using the probabilistic time series model.
The claimed invention still recites a method comprising (i) receiving data, (ii) organizing data and (iii) predicting a future return using a model.
This is analogous to Example 47: Anomaly Detection, Claim 2, wherein a method comprising receiving training data, organizing the training data, analyzing data anomalies using a trained model and outputting a determination pertaining to the data anomalies by the trained model was still deemed a mental process, even after exclusion of the method step of training the model from consideration. See July 2024 Subject Matter Eligibility Examples, pp. 7-8.
Applicant further argues:
Similarly, in the instant case, the limitations reciting training specific machine learning models in a specific manner with specific types of data in claim 1 do not recite any judicial exception because they do not set forth any 'mathematical relationships, calculations, formulas, or equations using words or mathematical symbols', as required by the August 2025 USPTO Memo. See Arguments, p. 11.
The Examiner respectfully disagrees.
The claimed invention recites 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 and predicts a probability distribution of a return of the asset.
Adjusting a mean of the probabilistic time series is a mathematical relationship, calculation, formula, or equation using words or mathematical symbols.
This is analogous to Example 47: Anomaly Detection, Claim 2, wherein a method comprising training a machine learning model utilizing a backpropagation algorithm and a gradient descent algorithm was deemed to recite “specific mathematical calculations” and, as such, encompassed a mathematical concept. See July 2024 Subject Matter Eligibility Examples, pp. 7-8.
Applicant further argues:
The Office Action alleges that the subject matter of claim 1 can be classified as mitigating risk because "predicting the future return of an asset is mitigation of financial risk [which is] a court-provided example of a fundamental economic practice". See Office Action at p. 6. Applicant submits that predicting the future return of an asset does not amount to any kind of mitigation of financial risk. See Arguments, p. 11.
The Examiner respectfully disagrees.
The specification recites:
It can be further understood from the description herein that implementations of the invention may be used to provide a computer-implemented method comprising: determining impact of stochastic events on a finance time series using a dynamic variance-covariance matrix by developing a set of probabilistic models, and learning historical impacts on events for a given time period (e.g., density of event occurrence and causal relationship); estimating asset risk based on the dynamic variance-covariance matrix; and optimizing a portfolio based on the estimated asset risk. See para. 121 – emphasis added.
Estimating asset risk to enable optimize a portfolio based on the estimated asset risk is mitigation of financial risk, optimization of the portfolio is to mitigate the financial risk presented by the asset.
Even if “predicting the future return of an asset does not amount to any kind of mitigation of financial risk,” predicting the future return of an asset is a fundamental economic practice. Predicting the future return of an asset (i.e., financial analysis) is a fundamental economic practice.
Applicant further argues:
In the instant case, the limitations reciting training specific machine learning models with time series data of asset prices and stochastic event data related to the asset, and then predicting a future return of the asset using such machine learning models neither amounts to any kind of intermediated settlement nor any kind of risk hedging. As such, contrary to the Examiner's assertions, claim 1 does not recite mitigating risk within the context of organizing human activity, as required under § 101 analysis. See Arguments, p. 11.
The Examiner respectfully disagrees.
As with the Examiner’s previous response to Applicant’s arguments pertaining to the mental process classification, even if training a machine learning model is excluded from consideration, the remainder of the claimed invention still recites a fundamental economic practice.
The claimed invention still recites a method comprising:
receiving, by a processor set, user input from a graphical user interface of a user device, 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;
creating, by the processor set, a training data set based on the received user input, the training data set including time series data of a price of the asset and stochastic event data of events related to the asset;
…
predicting, by the processor set, a future return of the asset for the future time period using the probabilistic time series model.
Predicting the future return of an asset (i.e., financial analysis) is a fundamental economic practice.
Step 2A Prong Two
Applicant argues that the claimed invention recites a practical application, specifically “an improvement in the functioning of a computer, or an improvement to other technology or technical field,” and, as such, satisfies Step 2A Prong Two of the §101 Guidelines. See Arguments, pp. 12-17.
Specifically, Applicant argues:
The Office Action fails to consider the particularity of the technical solution and details of how the technical solution is accomplished recited in the claims, as required by MPEP 2106.05(f). In the present application, the claims recite a particular solution (portfolio optimization using a probabilistic forecasting framework for time series data and stochastic event data) to a particular problem (inability to account for sophisticated temporal interactions among different stochastic events and their shock effects to time series, which can also have temporal cross-correlations for multivariate time series) to achieve a desired outcome (predicting future return of an asset using the probabilistic forecasting framework that captures the inter- dependencies among stochastic events, and the impact of these events on time series data). See Arguments, p. 12 – emphasis added.
The Examiner respectfully disagrees.
In DDR Holdings, LLC v. Hotels.com, the U.S. Court of Appeals stated:
As an initial matter, it is true that the claims here are similar to the claims in the cases discussed above in the sense that the claims involve both a computer and the Internet. But these claims stand apart because they do not merely recite the performance of some business practice known from the pre-Internet world along with the requirement to perform it on the Internet. Instead, the claimed solution is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks. See DDR Holdings, LLC v. Hotels.com, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) – emphasis added.
In the instant case, the problem that the claimed invention is designed to overcome, an “inability to account for sophisticated temporal interactions among different stochastic events and their shock effects to time series, which can also have temporal cross-correlations for multivariate time series,” is not a problem specifically arising from the realm of computers. This problem is a standard problem that exists outside the realm of computers and existed before the age of computers.
Admittedly, the claimed invention is a “technical solution,” as the claimed invention is utilizing technology to perform “portfolio optimization using a probabilistic forecasting framework for time series data and stochastic event data” but utilization of technology is not analogous to improving technology or solving a technology-based problem.
The court in Electric Power Group LLC v. Alstom SA (Fed. Cir. 2016) stated:
The claims here are unlike the claims in Enfish. There, we relied on the distinction made in Alice between, on one hand, computer-functionality improvements and, on the other, uses of existing computers as tools in aid of processes focused on “abstract ideas” (in Alice, as in so many other § 101 cases, the abstract ideas being the creation and manipulation of legal obligations such as contracts involved in fundamental economic practices). Enfish, 822 F.3d at 1335-36; see Alice, 134 S. Ct. at 2358-59. That distinction, the Supreme Court recognized, has common-sense force even if it may present line-drawing challenges because of the programmable nature of ordinary existing computers. In Enfish, we applied the distinction to reject the § 101 challenge at stage one because the claims at issue focused not on asserted advances in uses to which existing computer capabilities could be put, but on a specific improvement—a particular database technique—in how computers could carry out one of their basic functions of storage and retrieval of data. Enfish, 822 F.3d at 1335-36; see Bascom, 2016 U.S. App. LEXIS 11687, 2016 WL 3514158, at *5; cf. Alice, 134 S. Ct. at 2360 (noting basic storage function of generic computer). The present case is different: the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools. see Electric Power Group LLC v. Alstom SA, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) – emphasis added.
The claimed invention is not an improvement to computer technology or computer functionality. Rather, the claimed invention is applying a computer’s existing capabilities to implement a particular abstract idea. As in Electric Power Group, the focus of the claimed invention is not on an improvement in computers as tools but on improving an abstract idea (i.e., portfolio optimization using a probabilistic forecasting framework for time series data and stochastic event data) that uses computers as tools.
§103 Rejection
The §102 and/or §103 Rejection has been rewritten and the prior art remapped to account for the newly amended claim language.
6. Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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 May 12, 2026
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