DETAILED ACTION
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10-16-2025 has been entered.
Status of Claims
This action is a non-final rejection
Claims 1-4, 6-15, 21-24, 27-28 are pending
Claims 5, 16-20, 25-26 were cancelled
Claims 1 and 21 were amended
Claims 1-4, 6-15, 21-24, 27-28 are rejected under 35 USC § 101
Priority
Acknowledgement is made of Applicant’s claim for a domestic priority date of 8-8-2016
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 1-4, 6-15, 21-24, 27-28 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-4, 6-15, 21-24, 27-28, the claims recite an abstract idea of generating and reconfiguring optimal permissible solutions for financial trading based on the generation of cross graphs.
Independent Claims 1 and 21 are rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claims 1 and 21 recite a method for creating, training and reconfiguring a cross engine for financial trading.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
receive an initial situation specifying at least a variable set associated with a plurality of market participants, wherein each market participant is associated with at least one product that the market participant has or wants;
generate, …, a cross graph for each product to produce a plurality of cross graphs, each cross graph specifying a volume of the product available to sell to those market participants wanting the product by those market participants having the product and store the plurality of cross graphs;
generate, based at least in part on the plurality of cross graphs, a set of permissible solutions, each permissible solution of the set of permissible solutions specifying an exact volume of product that may be either bought or sold by each market participant and store the set of permissible solutions;
provide the initial situation;
receive, as output based on the initial situation, a set of recommended trades and store the set of recommended trades;
generate a reinforced set of recommended trades by marking selected recommended trades from the set of recommended trades output with positive or negative reinforcements;
verify that the set of recommended trades output falls within the set of permissible solutions, identify in the set of recommended trades output any impermissible recommended trades that do not fall within the set of permissible solutions, and mark the impermissible recommended trades with a negative reinforcement in the reinforced set of recommended trades;
filter the set of recommended trades output according to a new constraint, mark any recommended trades, from the set of recommended trades output, that violate the new constraint with a negative reinforcement in the reinforced set of recommended trades, and mark any recommended trades, from the set of recommended trades output, that adhere to the new constraint with a positive reinforcement in the reinforced set of recommended trades;
mark selected recommended trades of the set of recommended trades output with a positive reinforcement or a negative reinforcement in the reinforced set of recommended trades in accordance with an optimization specification;
regenerate based on the reinforced set of recommended trades and the initial situation by removing one or more of constraints of the set of recommended trades that is tuned according to one or more optimization parameters related to a set of reinforcement learning rules for the positive reinforcement or the negative reinforcement in the reinforced set of recommended trades,
receive a new situation specifying a market participant of the plurality of market participants, wherein the market participant is associated with at least one product that the market participant has or wants;
apply the new situation to generate a new set of recommended trades for the market participant.
belongs to the grouping of certain methods of organizing human activity under fundamental economic principles or practices as it recites generating and reconfiguring optimal permissible solutions for financial trading based on the generation of cross graphs. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). Claims 1 and 21 recites:
A computer implemented method for generating a trained crossing engine, embodied as instructions stored in non-transitory computer memory which, when executed by a computer processor;
a crossing engine generation component executed by the computer processor;
a cross graph database;
a cross graph solving component executed by the computer processor, … stored in the cross graph database;
a permissible solutions database;
generate, by a crossing engine generation component executed by the computer processor, based at least in part on (i) the variable set associated with the initial situation and (ii) the set of permissible solutions stored in the permissible solutions database;
an initial crossing engine, the initial crossing engine comprising a trained machine learning algorithm for a deep learning task;
initial crossing engine;
recommended trades database;
stored in the permissible solutions database;
via two or more iterations of reinforcement machine learning over time the initial crossing engine .. during the two or more iterations of the reinforcement learning to obtain a revised crossing engine;
the revised crossing engine comprising a retrained machine learning algorithm to improve the deep learning task for the retrained machine learning algorithm as compared to the trained machine learning algorithm for the initial crossing engine, wherein the two or more iterations of the reinforcement machine learning is executed without regeneration of the variable set associated with the initial situation to minimize computational resources for the computer processor;
the retrained machine learning algorithm of the revised crossing engine;
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
This judicial exception is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of financial trading in a computer environment. The additional elements are directed to generically recited computer elements of a non-transitory computer memory, an engine, processor, database, and generic machine learning algorithm. The additional elements are simply implementing the abstract idea on a generic computer are not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two, claims 1 and 21 recite:
A computer implemented method for generating a trained crossing engine, embodied as instructions stored in non-transitory computer memory which, when executed by a computer processor;
a crossing engine generation component executed by the computer processor;
a cross graph database;
a cross graph solving component executed by the computer processor, … stored in the cross graph database;
a permissible solutions database;
generate, by a crossing engine generation component executed by the computer processor, based at least in part on (i) the variable set associated with the initial situation and (ii) the set of permissible solutions stored in the permissible solutions database;
an initial crossing engine, the initial crossing engine comprising a trained machine learning algorithm for a deep learning task;
initial crossing engine;
recommended trades database;
stored in the permissible solutions database;
via two or more iterations of reinforcement machine learning over time the initial crossing engine .. during the two or more iterations of the reinforcement learning to obtain a revised crossing engine;
the revised crossing engine comprising a retrained machine learning algorithm to improve the deep learning task for the retrained machine learning algorithm as compared to the trained machine learning algorithm for the initial crossing engine, wherein the two or more iterations of the reinforcement machine learning is executed without regeneration of the variable set associated with the initial situation to minimize computational resources for the computer processor;
the retrained machine learning algorithm of the revised crossing engine;
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)) Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of financial trading in a computer environment. Thus, even when viewed separately and as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claim is ineligible
Dependent Claims:
Step 2A Prong One: The following dependent claims recites additional limitations that further define the abstract idea of generating and reconfiguring optimal permissible solutions for financial trading based on the generation of cross graphs. The claim limitations include:
Claim 2: wherein the initial situation specifies at least one trade constraint.
Claim 3: wherein the initial situation specifies at least one portfolio constraint.
Claim 4: apply a constraint to the set of recommended trades to produce a constrained set of recommended trades.
Claim 6: remove a constraint on the set of recommended trades to produce a less constrained set of recommended trades
Claim 7: wherein a cross graph comprises one or more nodes and one or more edges;
Claim 8: wherein a node represents a market participant;
Claim 9: wherein an edge connects a first market participant and a second market participant;
Claim 10: wherein an edge represents a number of products that may be traded from a first market participant to a second market participant.
Claim 11: wherein an edge comprises an edge weight.
Claim 12: wherein an edge weight is assigned based on output from a random number generator.
Claim 13: wherein a product comprises a security
Claim 14: wherein a product is identified by one of a CUSIP designation or a ISIN designation;
Claim 15: deliver the set of recommended trades for rendering;
Claim 22: further comprising algorithmically generating the initial situation;
Claim 23: wherein the subset of permissible solutions is algorithmically generated.
Claim 27: wherein the subset of permissible solutions is not exhaustive of all permissible solutions.
Claim 28: wherein the exact volume of product that may be either bought or sold by each market participant for each permissible solution in the subset of permissible solutions is generated by a random number generator
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims include:
Claim 4: wherein the instructions stored in non-transitory computer memory, when executed by a computer processor are further configured to: regenerate the initial crossing engine based on the constrained set of recommended trades and the initial situation.
Claim 6: wherein the instructions stored in non-transitory computer memory, when executed by a computer processor, are further configured to: regenerate the initial crossing engine based on the less constrained set of recommended trades and the initial situation;
Claim 15: wherein the instructions stored in non- transitory computer memory, when executed by a computer processor, are further configured to: via a user interface;
Claim 23: by the cross graph solving component executed by the computer processor;
Claim 24: further comprising inputting into the permissible solutions database a permissible solution that was not generated by the cross graph solving component.
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claim 4: wherein the instructions stored in non-transitory computer memory, when executed by a computer processor are further configured to: regenerate the initial crossing engine based on the constrained set of recommended trades and the initial situation.
Claim 6: wherein the instructions stored in non-transitory computer memory, when executed by a computer processor, are further configured to: regenerate the initial crossing engine based on the less constrained set of recommended trades and the initial situation;
Claim 15: wherein the instructions stored in non- transitory computer memory, when executed by a computer processor, are further configured to: via a user interface;
Claim 23: by the cross graph solving component executed by the computer processor;
Claim 24: further comprising inputting into the permissible solutions database a permissible solution that was not generated by the cross graph solving component.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety
LUPIEN (WO 9634357 A1) - CROSSING NETWORK UTILIZING SATISFACTION DENSITY PROFILE -teaches: A crossing network that matches buy and sell orders based upon a satisfaction and quantity profile is disclosed. The crossing network includes a number of trader terminals that can be used for entering orders. The orders are entered in the form of a satisfaction density profile that represents a degree of satisfaction to trade a particular instrument at various (price, quantity) combinations. Typically, each order is either a buy order or a sell order. The trader terminals are coupled to a matching controller computer. The matching controller computer can receive as input the satisfaction density profiles entered at each one of the trading terminals. The matching controller computer matches orders (as represented by each trader's satisfaction density profile) so that each trader is assured that the overall outcome of the process (in terms of average price and size of fill) has maximized the mutual satisfaction of all traders. Typically, the matching process is anonymous. The matching process can be continuous or a batch process, or a hybrid of the two. Unmatched satisfaction density profiles can be used to provide spread and pricing information. Factors other than price and quantity also may be used to determine the degree of satisfaction. Optionally, priority may be given to certain profiles in the matching process to accommodate stock exchange rules, for example, requiring that priority be given to orders exhibiting the best price, regardless of size or any other consideration. The crossing network has utility both in the securities industry and for non-securities industry applications.
BLASER (US 20140081667 A1) - MARKET ENGINE HAVING OPTIMIZATION-teaches: A crossing method and apparatus for an e-commerce system for electronic transactions. Electronic transactions involve participants having interests in instruments to be crossed. A crossing unit stores variables for use in mapping the interests of the participants, stores constraints for limiting the mapping of interests of the participants and stores an expression relating the variables. The crossing unit operates to determine values of the variables that optimize the expression, while satisfying the constraints, thereby mapping the interests of the participants. The determining step communicates the variables to a solver tailored to optimize the expression, while satisfying the constraints, thereby mapping the interests of the participants..
Response to Arguments
Applicant's arguments filed 10/16/2025 have been fully considered but they are not persuasive.
Claims 1-4, 6-15, 21-24, 27-28 are pending. Applicant amended claims 1 and 21 as posted in the above analysis with additions underlined
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 101:
Several steps are taken in the analysis as to whether an invention is rejected under 101. The first step is to determine if the claim falls within a statutory category. In this case it does for claims 1 and 21 since the claims recite a method for creating, training and reconfiguring a cross engine for financial trading.
The second step under 2A prong one is to determine if the claims recite an abstract idea, which would be the case if the invention can be grouped as either: a) mathematical concepts; (b) mental processes; or (c) certain methods of organizing human activity (encompassing (i) fundamental economic principles, (ii) commercial or legal interactions or (iii) managing personal behavior or relationships or interactions between people). The current invention is classified as an abstract idea since it may be grouped as belonging to the grouping of certain methods of organizing human activity under fundamental economic principles or practices as it recites generating and reconfiguring optimal permissible solutions for financial trading based on the generation of cross graphs.
The third step under 2A Prong Two is to determine if additional elements in the claim imposes a meaningful limit on the abstract idea in order to integrate it into a practical idea. The current invention does not represent a practical idea since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
the fourth step under 2B is to determine if additional elements of the claim provide an inventive concept. An invention may be classified as an inventive concept if a computer-implemented processes is determined to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic, and non-conventional even if generic computer operations on a generic computing device is used to implement the abstract idea.
Regarding Step 2A Prong One:
The Applicant argues that the independent claims 1 and 21 are not directed to an abstract idea, and furthermore do not belong to "a fundamental economic principle"
Specifically the Applicant argues that the language of claim 1 goes beyond a mere "method of organizing human activity." For example, claim 1 recites, in part, "generate, by a crossing engine generation component executed by the computer processor, based at least in part on the initial situation and the set of permissible solutions stored in the permissible solutions database, an initial crossing engine, the initial crossing engine comprising a trained machine learning algorithm for a machine learning task”,"generate a reinforced set of recommended trades by marking selected recommended trades from the set of recommended trades output by the initial crossing engine with positive or negative reinforcements,"; "regenerate, via two or more iterations of reinforcement machine
learning over time, the initial crossing engine based on the reinforced set of
recommended trades and the initial situation by removing one or more of
constraints of the set of recommended trades during the two or more
iterations of the reinforcement learning to obtain a revised crossing engine that
is tuned according to one or more optimization parameters related to a set of reinforcement learning rules for the positive reinforcement or the negative reinforcement in the reinforced set of recommended trades, the revised crossing engine comprising a retrained machine learning algorithm to improve the
deep learning task for the retrained machine learning algorithm as compared
to the trained machine learning algorithm for the initial crossing engine,
wherein the two or more iterations of reinforcement machine learning is
executed without regeneration of the variable set associated with the initial
situation to minimize computational resources for the computer processor "; “receive a new situation specifying a market participant of the plurality of
market participants, wherein the market participant is associated with at least one
product that the market participant has or wants; and “apply the new situation to the retrained machine learning algorithm of the revised crossing engine to generate a new set of recommended trades for the market participant”
The Examiner disagrees since the Applicant’s argument is not persuasive. The following limitations are related to a process that could be carried out by a human without use of a computer and that relates to trading which may be characterized as a fundamental economic human activity. Hence the following limitations as listed below, belong to the grouping of certain methods of organizing human activity under fundamental economic principles or practices as it recites “generating and reconfiguring optimal permissible solutions for financial trading based on the generation of cross graphs” (refer to MPEP 2106.04(a)(2)).
The method used to select the abstract idea is to strip the additional elements from the claims. As seen below the recited boldened words constitute the abstract idea after stripping the un-boldened additional elements of amended limitation of claims 1 and 21:
A computer implemented method for generating a trained crossing engine, embodied as instructions stored in non-transitory computer memory which, when executed by a computer processor, are configured to:
receive an initial situation specifying at least a variable set associated with a plurality of market participants, wherein each market participant is associated with at least one product that the market participant has or wants;
generate, by a crossing engine generation component executed by the computer processor, a cross graph for each product to produce a plurality of cross graphs, each cross graph specifying a volume of the product available to sell to those market participants wanting the product by those market participants having the product and store the plurality of cross graphs in a cross graph database;
generate, by a cross graph solving component executed by the computer processor, based at least in part on the plurality of cross graphs stored in the cross graph database, a set of permissible solutions, each permissible solution of the set of permissible solutions specifying an exact volume of product that may be either bought or sold by each market participant and store the set of permissible solutions in a permissible solutions database;
generate, by a crossing engine generation component executed by the computer processor, based at least in part on (i) the variable set associated with the initial situation and (ii) the set of permissible solutions stored in the permissible solutions database, an initial crossing engine, the initial crossing engine comprising a trained machine learning algorithm for a deep learning task;
provide the initial situation to the initial crossing engine;
receive, as output from the initial crossing engine and based on the initial situation, a set of recommended trades and store the set of recommended trades in a recommended trades database;
generate a reinforced set of recommended trades by marking selected recommended trades from the set of recommended trades output by the initial crossing engine with positive or negative reinforcements;
verify that the set of recommended trades output by the initial crossing engine falls within the set of permissible solutions stored in the permissible solutions database, identify in the set of recommended trades output by the initial crossing engine any impermissible recommended trades that do not fall within the set of permissible solutions, and mark the impermissible recommended trades with a negative reinforcement in the reinforced set of recommended trades;
filter the set of recommended trades output by the initial crossing engine according to a new constraint, mark any recommended trades, from the set of recommended trades output by the initial crossing engine, that violate the new constraint with a negative reinforcement in the reinforced set of recommended trades, and mark any recommended trades, from the set of recommended trades output by the initial crossing engine, that adhere to the new constraint with a positive reinforcement in the reinforced set of recommended trades;
mark selected recommended trades of the set of recommended trades output by the initial crossing engine with a positive reinforcement or a negative reinforcement in the reinforced set of recommended trades in accordance with an optimization specification;
regenerate via two or more iterations reinforcement machine learning over time the initial crossing engine based on the reinforced set of recommended trades and the initial situation by removing one or more of constraints of the set of recommended trades during the two or more iterations of the reinforcement learning to obtain a revised crossing engine that is tuned according to one or more optimization parameters related to a set of reinforcement learning rules for the positive reinforcement or the negative reinforcement in the reinforced set of recommended trades, the revised crossing engine comprising a retrained machine learning algorithm to improve the deep learning task for the retrained machine learning algorithm as compared to the trained machine learning algorithm for the initial crossing engine, wherein the two or more iterations of the reinforcement machine learning is executed without regeneration of the variable set associated with the initial situation to minimize computational resources for the computer processor;
receive a new situation specifying a market participant of the plurality of market participants, wherein the market participant is associated with at least one product that the market participant has or wants;
apply the new situation to the retrained machine learning algorithm of the revised crossing engine to generate a new set of recommended trades for the market participant.
The concept of using the cross engine for financial trading is present not only in the title of the invention: “Methods, Systems and Apparatuses for Creating, Training and Reconfiguring a Cross Engine for Financial Trading”. It is also cited in the specification: trading of equities [0006]; the brief summary [0008]; optimal portfolios [0026]; desired trading volumes for various products [0034] as well as the claims themselves. Clearly the purpose of the invention regarding the training of a cross engine is for financial trading. As a result as listed in the 2A Prong One analysis claims 1 and 21 fall within one of the groupings of abstract ideas. Specifically they belong to the grouping of certain methods of organizing human activity under fundamental economic principles or practices as it recites generating and reconfiguring optimal permissible solutions for financial trading based on the generation of cross graphs. (refer to MPP 2106.04(a)(2)). Accordingly claims 1 and 21 recite an abstract idea.
Regarding Step 2A Prong Two and Step 2B:
The Applicant argues that even assuming the arguendo that the claims are directed to an abstract idea, the Applicant submits that the claims recite additional elements that integrate the judicial exception into a practical since they are directed to an improvement in the functioning of a computer. The Applicant cites several examples based on the amendments to demonstrate the improvement in technology including: "machine/deep learning" and "reinforcement learning" to improve functioning of a crossing engine configured for providing inferences related to recommended trades. Specifically the Applicant argues that claims 1 and 21 recite "generate, by a crossing engine generation component executed by the computer processor, based at least in part on the initial situation and the set of permissible solutions stored in the permissible solutions database, an initial crossing engine, the initial crossing engine comprising a trained machine learning algorithm for a machine learning task”,"generate a reinforced set of recommended trades by marking selected recommended trades from the set of recommended trades output by the initial crossing engine with positive or negative reinforcements,"; "regenerate, via two or more iterations of reinforcement machine
learning over time, the initial crossing engine based on the reinforced set of
recommended trades and the initial situation by removing one or more of
constraints of the set of recommended trades during the two or more
iterations of the reinforcement learning to obtain a revised crossing engine that
is tuned according to one or more optimization parameters related to a set of reinforcement learning rules for the positive reinforcement or the negative reinforcement in the reinforced set of recommended trades, the revised crossing engine comprising a retrained machine learning algorithm to improve the
deep learning task for the retrained machine learning algorithm as compared
to the trained machine learning algorithm for the initial crossing engine,
wherein the two or more iterations of reinforcement machine learning is
executed without regeneration of the variable set associated with the initial
situation to minimize computational resources for the computer processor "; “receive a new situation specifying a market participant of the plurality of
market participants, wherein the market participant is associated with at least one
product that the market participant has or wants; and “apply the new situation to the retrained machine learning algorithm of the revised crossing engine to generate a new set of recommended trades for the market participant”
Moreover, one or more elements of claim 1 "appl[y] or use the [alleged] judicial exception in some other meaningful way beyond generally linking the use of the [alleged] judicial exception to a particular technological environment." For example, claim 1 recites "apply the new situation to the retrained machine learning algorithm of the revised crossing engine to generate a new set of recommended trades for the market participant" that utilizes the "the retrained machine learning algorithm of the revised crossing engine" in a meaningful way to generate "a new set of recommended trades for the market participant." In this regard, the accumulated learning of the "revised crossing engine" is provided in a meaningful manner such that "differently configured crossing engines may be created."
The Examiner disagrees since the Applicant’s arguments are not persuasive. The above cited “alleged technological improvements” are all performed by a generic computer or processor. The Applicant fails to show otherwise.
Neither claims 1 or 21 recite additional elements that impose a meaningful limit on the abstract idea:
Claims 1 and 21 recite the following additional elements:
A computer implemented method for generating a trained crossing engine, embodied as instructions stored in non-transitory computer memory which, when executed by a computer processor;
a crossing engine generation component executed by the computer processor;
a cross graph database;
a cross graph solving component executed by the computer processor, … stored in the cross graph database;
a permissible solutions database;
generate, by a crossing engine generation component executed by the computer processor, based at least in part on (i) the variable set associated with the initial situation and (ii) the set of permissible solutions stored in the permissible solutions database;
an initial crossing engine, the initial crossing engine comprising a trained machine learning algorithm for a deep learning task;
initial crossing engine;
recommended trades database;
stored in the permissible solutions database;
via two or more iterations of reinforcement machine learning over time the initial crossing engine ..
during the two or more iterations of the reinforcement learning to obtain a revised crossing engine;
the revised crossing engine comprising a retrained machine learning algorithm to improve the deep learning task for the retrained machine learning algorithm as compared to the trained machine learning algorithm for the initial crossing engine, wherein the two or more iterations of the reinforcement machine learning is executed without regeneration of the variable set associated with the initial situation to minimize computational resources for the computer processor;
the retrained machine learning algorithm of the revised crossing engine
The additional elements as recited above amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly the claim as a whole does not integrate the abstract idea into a practical application, nor does it provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible and hence the claims remain rejected under 35 U.S.C. 101
Further support regarding mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea may be seen in paragraphs [0052- 0070], of the specification.
In order to integrate the abstract idea into a practical idea the Applicant could demonstrate that at least one of the conditions enumerated below applies:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
The Applicant has not demonstrated any of the above listed conditions.
The analysis of Step 2B is similar to Step 2A Prong Two in that the additional elements as recited in claims 1 and 21 amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
In order evaluate whether the claim recites additional elements that amount to an inventive concept what could be shown is:
Adding a specific limitation (unconventional other than what is well-understood, routine, conventional (WURC) activity in the field - see MPEP 2106.05(d)
The Applicant has not demonstrated the above listed condition.
In conclusion for reasons of record and as set forth above, the examiner maintains the rejection of claims 1-4, 6-15, 21-24, 27-28 as being directed to a judicial exception without significantly more, and thereby being directed to non-statutory subject matter under 35 USC §101. In reaching this decision, the Examiner considered all evidence presented and all arguments actually made by Applicant.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE L MACCAGNO whose telephone number is (571)270-5408. The examiner can normally be reached M-F 8:00 to 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571)270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687