DETAILED ACTION
Notice of 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 .
Status of Claims
This action is in reply to the first amendment to non-final filed on October 24, 2025.
Claims 1 and 15–18 have been amended and are hereby entered.
Claims 4–11 have been canceled.
Claims 1–3 and 12–18 are currently pending and have been examined.
This action is made FINAL.
Response to Amendment
The amendment filed October 24, 2025 has been entered. Claims 1–3 and 12–18 remain pending in the application.
Claim Rejections - 35 USC § 101
The following is a quotation of 35 U.S.C. 101:
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 1–3 and 12–18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First of all, claims must be directed to one or more of the following statutory categories: a process, a machine, a manufacture, or a composition of matter. Claims 1–3 and 12–14 are directed to a machine (“A system”). Thus, claims 1–3 and 12–14 satisfy Step One because they are all within one of the four statutory categories of eligible subject matter.
Claims 1–3 and 12–14, however, are directed to an abstract idea without significantly more. For claim 1, the specific limitations that recite an abstract idea are:
. . . computing a plurality of computed security . . . trading price reactions to a recent . . . trade of a security at subsequent points in time;
. . . real-time . . . trading orders of said security and real-time . . . trading price data of said security, for developing an . . . predictive model of . . . trading timing price volatility for said security based on said plurality of computed security . . . trading price reactions, a historical security price data, a real-time security . . . trading price data, a historical security . . . trading order data, and a real-time security . . . trading order data;
. . . said predictive model to predict a predicted . . . trading matching time window of . . . trading price volatility for said security below a price volatility threshold; and
. . . matching an . . . trading bid for said security with an . . . trading offer for said security to form an . . . trade of said security, delaying the execution of said trade of said security based on said predicted . . . trading matching time window of . . . trading price volatility for said security, and for executing said . . . trade of said security within said predicted . . . trading matching time window of . . . trading price volatility for said security.
The claims, therefore, recite analyzing and executing a security trade, which is the abstract idea of certain methods of organizing human activity because they recite a commercial interaction. The claims also recite calculating market reactions and matching parameters from market data, which is the abstract idea of mathematical concepts because they recites a mathematical relationship.
The judicial exception recited above is not integrated into a practical application. The additional elements of the claims are various generic technologies and computer components to implement this abstract idea (“market response module”, “processor”, “machine learning engine”, and “matching engine”). These additional elements are not integrated into a practical application because the invention merely applies the abstract idea to generic computer technology, using the computer to assess data and perform a transaction. Claim 1 does introduce more specific technology, with machine learning, but again, this is merely being used as a generic tool to implement the abstract idea above. The machine learning process only provide an alternative means for analyzing the market data, rather than creating any type of improvement to the technology itself. Because the invention is using the computer simply as a tool to perform the abstract idea on, the judicial exception is not integrated into a practical application.
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements in combination are at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic components. Because merely “applying” the exception using generic computer components cannot provide an inventive concept, the additional elements do not recite significantly more than the judicial exception. Thus, claim 1 is not patent eligible.
Independent claims 12 is rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent system claim 1. Claim 12 merely further narrows the abstract idea discussed above, by specifying that an order size is determined based on price volatility rather than a time window. The limitations of this claim fail to integrate the abstract idea into a practical application because there are no additional elements recited in this claim other than the generic technology and computer parts discussed above (“market response module”, “processor”, “machine learning engine”, and “matching engine”). And, again, the machine learning engine only provide an alternative means for analyzing the market data, rather than creating any type of improvement to the technology itself. This dependent claim, therefore, also amounts to merely using a computer, in its ordinary capacity, as a tool to perform the abstract idea. Finally, the additional recited limitations of this dependent claim fails to establish that the claim provides an inventive concept because claims that merely use a computer, in its ordinary capacity, as a tool to perform the abstract idea cannot provide an inventive concept. Thus, claim 12 is also not patent eligible.
Dependent claims 2, 3, 13, and 14 have been given the full two part analysis, analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually and in combination, are also held to be patent ineligible under 35 U.S.C. 101.
For claims 2, 3, 13, and 14, the additional recited limitations of these claims merely further narrow the abstract idea discussed above. These dependent claims only narrow the matching parameter calculation recited in claim 1 by further specifying what it is used for—“reduce an adverse selection” and “reduce a market impact”. The limitations of these claims fail to integrate the abstract idea into a practical application because these claims do not introduce additional elements other than the generic components discussed above (“machine learning engine”). Claims 2, 3, 13, and 14 do introduce more specific technology, with machine learning, but again, this is merely being used as a generic tool to implement the abstract idea above. The machine learning process only provide an alternative means for analyzing the market data, rather than creating any type of improvement to the technology itself. These dependent claims, therefore, also amount to merely using a computer, in its ordinary capacity, as a tool to perform the abstract idea. Finally, the additional recited limitations of these dependent claims fail to establish that the claims provide an inventive concept because claims that merely use a computer, in its ordinary capacity, as a tool to perform the abstract idea cannot provide an inventive concept.
For claims 15–18, the additional recited limitations of these claims merely further narrow the abstract idea discussed above. These dependent claims only narrow the matching parameter calculation recited in claim 1 by further specifying the model used—“reinforcement . . . methodology” and “supervised . . . methodology”. The limitations of these claims fail to integrate the abstract idea into a practical application because these claims do not introduce additional elements other than the generic components discussed above (“machine learning engine”). Claims 15–18 do introduce more specific technology, with machine learning, but again, this is merely being used as a generic tool to implement the abstract idea above. These claims merely specify the machine learning methodology used by reciting it generally, rather than specifying how the machine learning operates. The machine learning processes, therefore, still only provide an alternative means for analyzing the market data, rather than creating any type of improvement to the technology itself. Thus, these dependent claims also amount to merely using a computer, in its ordinary capacity, as a tool to perform the abstract idea. Finally, the additional recited limitations of these dependent claims fail to establish that the claims provide an inventive concept because claims that merely use a computer, in its ordinary capacity, as a tool to perform the abstract idea cannot provide an inventive concept.
Response to Arguments
Claim Rejections Under 35 U.S.C. § 101
Applicant’s arguments filed on October 24, 2025 have been fully considered but they are not persuasive.
Applicant argues that the claims are not directed to an abstract idea and are instead integrated into a practical application. Applicant first explains that the matching engine recited in the claims is not generic technology because the claims have not been rejected under 35 U.S.C. 103. The analysis for patent eligibility under 35 U.S.C. 101, however, is distinct from the analysis for patentability under 35 U.S.C. 102 and 103. See MPEP 2106.05(I) (“Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101.”). Applicant next argues that the machine learning engine develops and uses the electronic predictive model, which is an improvement in technology. Applicant responds that the machine learning engine is therefore not a generic tool and cannot be a method of organizing human activity or a mental process. The claims as a whole, however, do recite these abstract ideas, even if the machine learning engine is an additional element not included within them. The machine learning model is then merely applied to these abstract ideas, as a tool to improve them and make them more efficient. Applicant further indicates that an improved electronic predictive model is recited because it determines timing price volatility and order size price volatility. These limitations, however, merely specify what information is determined with regard to executing the security trade, rather than reciting how the technology itself is improved in any way. Finally, Applicant argues that the claims do indicate the technology improvement, as explained in specification paragraphs 41 and 53. The specification paragraphs indicated by Applicant, however, merely restate the machine learning processes recited in the claims and the data used, and then state that conventional matching logic does not adapt to such changing market conditions. These improvements again are merely improvements to the abstract idea, to make more efficient determinations, rather than an improvement to the machine learning technology itself. Thus, claims 1–3 and 12–18 do recite an abstract idea and do not recite additional elements sufficient to integrate the claims into a practical application.
Prior Art Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Those prior art references are as follows:
Sapir et al., U.S. Patent App. No. 2012/0166326, discloses trading based on volatility thresholds.
Ondyak et al., U.S. Patent App. No. 2008/0243668, discloses tracking real time market data including current and historic trade prices and quotes.
UR, U.S. Patent App. No. 2017/0330073, discloses a neural network for predicting price volatility.
Myr, U.S. Patent App. No. 2005/0015323, discloses a machine learning model for predicting prices of securities.
Barr et al., U.S. Patent No. 5,761,442, discloses a system and method for selecting securities based on modelling by artificial neural networks.
Li, U.S. Patent App. No. 2016/0217366, discloses a method for selecting stocks using neural networks and ranking algorithms.
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.
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/DIVESH PATEL/Examiner, Art Unit 3696