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 .
DETAILED ACTION
Status of Claims
This action is in response to the application filed on July 30, 2024.
The instant application claims priority to application 18/112582 (now U.S. Patent 12,079,705) filed February 22, 2023 and application 15/467,632 (now U.S. Patent 11,625,569) filed March 23, 2017.
Claims 1-21 are currently pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 (see claim 21), sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-9 and 26 of U.S. Patent No. 11,625,569. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are fully encompassed by the claims in patent 11,625,569.
Instant application 18/788,868
U.S. Patent 11,625,569
1. A computer implemented method comprising:
encoding, by a processor coupled with a data transaction processing system, using a plurality of first layers of a layered plurality of interconnected processing nodes of a structured neural network, historic participant transaction data for a participant in the data transaction processing system and historic external market factor data including data indicative of characteristics of a financial derivative product traded on an exchange for a time period that corresponds to the historic participant transaction data that occurs during the time period, wherein at least a subset of the interconnections of the layered plurality of interconnected processing nodes are dynamically weighted;
decoding, by the processor, the encoded data using a plurality of second layers of the layered plurality of interconnected processing nodes of the structured neural network;
comparing, by the processor, using the structured neural network, the decoded data with the historic participant transaction data and the historic external market factor data;
identifying, by the processor, using the structured neural network, one or more patterns in the layered plurality of interconnected processing nodes when the decoded data is within a predefined distance of the historic participant transaction data and the historic external market factor data;
receiving, by the processor, data indicative of a new transaction;
generating, by the processor, a level of deviation by comparing the new transaction and current external market factor data with the one or more patterns; and
taking an action, by the processor, when the level of deviation exceeds a threshold.
2. The computer implemented method of claim 1, wherein the action includes:
generating, by the processor, an alert when the level of deviation exceeds the threshold.
1. A computer implemented method comprising:
detecting a deviation from prior activity, inconsistent with current conditions, by a participant in a data transaction processing system in which data items are transacted by a hardware matching processor that matches electronic data transaction request messages for the same one of the data items based on multiple transaction parameters from different client computers over a data communication network by:
identifying, by an activity module, in a memory, historic participant transaction data for the participant and historic external market factor data, wherein the historic external market factor data includes data indicative of characteristics of previous transactions by other participants and characteristics of a financial derivative product for a time period that corresponds to the historic participant transaction data that occurs during the time period, wherein the financial derivative product is traded on an exchange;
identifying, by the activity module, one or more patterns in the historic participant transaction data and the historic external market factor data using a structured neural network, the structured neural network comprising a layered plurality of interconnected processing nodes, wherein each connection of the plurality of interconnected processing nodes to another is dynamically weighted, the one or more patterns indicative of historical normal activity by the participant in relation to the historic external market factor data;
receiving, by the activity module, from the participant, an electronic data transaction request message comprising data indicative of a current transaction specifying a product, quantity, and value, and storing the electronic data transaction request message in the memory;
calculating, by the activity module, current external market factor data;
comparing, by the activity module, the data indicative of the transaction and the current external market factor data with the one or more patterns;
generating, by the activity module, an abnormality score for the electronic data transaction request message based on the comparison; and
generating, by the activity module, an alert when the abnormality score exceeds a first threshold.
2. The computer implemented method of claim 1, wherein identifying the one or more patterns comprises:
encoding, by the structured neural network, the historic participant transaction data and historic external market factor data using a plurality of first layers of the layered plurality of interconnected processing nodes;
decoding, by the structured neural network, the encoded data using a plurality of second layers of the layered plurality of interconnected processing nodes;
comparing, by the structured neural network, the decoded data with the historic participant transaction data and historic external market factor data; and
identifying, by the structured neural network, the one or more patterns in the layered plurality of interconnected processing nodes when the decoded data is within a predefined distance of the historic participant transaction data and historic external market factor data.
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11. A computer system comprising:
a processor coupled with a data transaction processing system;
a non-transitory computer-readable medium coupled with the processor, the non-transitory computer-readable medium storing computer-executable instructions executable by the computer system to cause the processor to:
encode, using a plurality of first layers of a layered plurality of interconnected processing nodes of a structured neural network, historic participant transaction data for a participant in a data transaction processing system and historic external market factor data including data indicative of characteristics of a financial derivative product traded on an exchange for a time period that corresponds to the historic participant transaction data that occurs during the time period, wherein at least a subset of the interconnections of the layered plurality of interconnected processing nodes are dynamically weighted;
decode the encoded data using a plurality of second layers of the layered plurality of interconnected processing nodes of the structured neural network;
compare, by the processor, using the structured neural network, the decoded data with the historic participant transaction data and the historic external market factor data;
identify, using the structured neural network, one or more patterns in the layered plurality of interconnected processing nodes when the decoded data is within a predefined distance of the historic participant transaction data and the historic external market factor data;
receive data indicative of a new transaction;
generate a level of deviation based on a comparison of the data indicative of the new transaction and current external market factor data with the one or more patterns; and
taking an action when the level of deviation exceeds a threshold.
12. The computer system of claim 11, wherein the action includes:
generate an alert when the level of deviation exceeds the threshold.
1. A computer implemented method comprising:
detecting a deviation from prior activity, inconsistent with current conditions, by a participant in a data transaction processing system in which data items are transacted by a hardware matching processor that matches electronic data transaction request messages for the same one of the data items based on multiple transaction parameters from different client computers over a data communication network by:
identifying, by an activity module, in a memory, historic participant transaction data for the participant and historic external market factor data, wherein the historic external market factor data includes data indicative of characteristics of previous transactions by other participants and characteristics of a financial derivative product for a time period that corresponds to the historic participant transaction data that occurs during the time period, wherein the financial derivative product is traded on an exchange;
identifying, by the activity module, one or more patterns in the historic participant transaction data and the historic external market factor data using a structured neural network, the structured neural network comprising a layered plurality of interconnected processing nodes, wherein each connection of the plurality of interconnected processing nodes to another is dynamically weighted, the one or more patterns indicative of historical normal activity by the participant in relation to the historic external market factor data;
receiving, by the activity module, from the participant, an electronic data transaction request message comprising data indicative of a current transaction specifying a product, quantity, and value, and storing the electronic data transaction request message in the memory;
calculating, by the activity module, current external market factor data;
comparing, by the activity module, the data indicative of the transaction and the current external market factor data with the one or more patterns;
generating, by the activity module, an abnormality score for the electronic data transaction request message based on the comparison; and
generating, by the activity module, an alert when the abnormality score exceeds a first threshold.
2. The computer implemented method of claim 1, wherein identifying the one or more patterns comprises:
encoding, by the structured neural network, the historic participant transaction data and historic external market factor data using a plurality of first layers of the layered plurality of interconnected processing nodes;
decoding, by the structured neural network, the encoded data using a plurality of second layers of the layered plurality of interconnected processing nodes;
comparing, by the structured neural network, the decoded data with the historic participant transaction data and historic external market factor data; and
identifying, by the structured neural network, the one or more patterns in the layered plurality of interconnected processing nodes when the decoded data is within a predefined distance of the historic participant transaction data and historic external market factor data.
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21. A computer system comprising:
means for encoding, using a plurality of first layers of a layered plurality of interconnected processing nodes of a structured neural network, historic participant transaction data for a participant in a data transaction processing system and historic external market factor data including data indicative of characteristics of a financial derivative product traded on an exchange for a time period that corresponds to the historic participant transaction data that occurs during the time period, wherein at least a subset of the interconnections of the layered plurality of interconnected processing nodes are dynamically weighted;
means for decoding the encoded data using a plurality of second layers of the layered plurality of interconnected processing nodes of the structured neural network;
means for comparing, using the structured neural network, the decoded data with the historic participant transaction data and the historic external market factor data;
means for identifying, using the structured neural network, one or more patterns in the layered plurality of interconnected processing nodes when the decoded data is within a predefined distance of the historic participant transaction data and the historic external market factor data;
means for receiving data indicative of a new transaction;
means for generating a level of deviation by comparing the new transaction and current external market factor data with the one or more patterns; and
means for taking an action, when the level of deviation exceeds a threshold.
26. A computer system for detecting a deviation from prior activity, inconsistent with current conditions, by a participant, the computer system comprising:
means for storing historic participant transaction data for the participant and historic external market factor data, wherein the historic external market factor data include data indicative of characteristics of previous transactions by other participants and characteristics of a financial derivative product for a time period that corresponds to the historic participant transaction data that occurs during the time period, wherein the financial derivative product is traded on an exchange;
means for identifying one or more patterns in the historic participant transaction data and the historic external market factor data using a structured neural network, the structured neural network comprising a layered plurality of interconnected processing nodes, wherein each connection of the plurality of interconnected processing nodes to another is dynamically weighted, the one or more patterns indicative of historical normal activity by the participant in relation to the historic external market factor data;
means for receiving from the participant, an electronic data transaction request message comprising data indicative of a current transaction specifying a product, quantity, and value, and storing the electronic data transaction request message in the computer system;
means for calculating current external market factor data;
means for comparing the data indicative of the transaction and the current external market factor data with the one or more patterns;
means for generating an abnormality score for the electronic data transaction request message based on the comparison; and
means for generating an alert when the abnormality score exceeds a first threshold.
Claims 1-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-9 and 21 of U.S. Patent No. 12,079,705. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are fully encompassed by the claims in patent 12,079,705.
Instant application 18/788,868
U.S. Patent 12,079,705
1. A computer implemented method comprising:
encoding, by a processor coupled with a data transaction processing system, using a plurality of first layers of a layered plurality of interconnected processing nodes of a structured neural network, historic participant transaction data for a participant in the data transaction processing system and historic external market factor data including data indicative of characteristics of a financial derivative product traded on an exchange for a time period that corresponds to the historic participant transaction data that occurs during the time period, wherein at least a subset of the interconnections of the layered plurality of interconnected processing nodes are dynamically weighted;
decoding, by the processor, the encoded data using a plurality of second layers of the layered plurality of interconnected processing nodes of the structured neural network;
comparing, by the processor, using the structured neural network, the decoded data with the historic participant transaction data and the historic external market factor data;
identifying, by the processor, using the structured neural network, one or more patterns in the layered plurality of interconnected processing nodes when the decoded data is within a predefined distance of the historic participant transaction data and the historic external market factor data;
receiving, by the processor, data indicative of a new transaction;
generating, by the processor, a level of deviation by comparing the new transaction and current external market factor data with the one or more patterns; and
taking an action, by the processor, when the level of deviation exceeds a threshold.
2. The computer implemented method of claim 1, wherein the action includes:
generating, by the processor, an alert when the level of deviation exceeds the threshold.
1. A computer implemented method comprising:
identifying, by a processor coupled with a data transaction processing system, using a structured neural network comprising a layered plurality of interconnected processing nodes, one or more patterns in historic participant transaction data for a participant in the data transaction processing system and historic external market factor data including data indicative of characteristics of a financial derivative product traded on an exchange for a time period that corresponds to the historic participant transaction data that occurs during the time period, the one or more patterns indicative of a historical normal activity by the participant in relation to the historic external market factor data, wherein at least a subset of the interconnections of the layered plurality of interconnected processing nodes are dynamically weighted;
receiving, by the processor, from the participant, data indicative of a new transaction;
calculating, by the processor, current external market factor data;
comparing, by the processor, the data indicative of the new transaction and the current external market factor data with the one or more patterns; generating, by the processor, an abnormality score for the new transaction based on the comparison; and
generating, by the processor, an alert when the abnormality score exceeds a first threshold.
2. The computer implemented method of claim 1, wherein identifying the one or more patterns comprises:
encoding, by the processor, using the structured neural network, the historic participant transaction data and the historic external market factor data using a plurality of first layers of the layered plurality of interconnected processing nodes; decoding, by the processor, using the structured neural network, the encoded data using a plurality of second layers of the layered plurality of interconnected processing nodes;
comparing, by the processor, using the structured neural network, the decoded data with the historic participant transaction data and the historic external market factor data; and identifying, by the processor, using the structured neural network, the one or more patterns in the layered plurality of interconnected processing nodes when the decoded data is within a predefined distance of the historic participant transaction data and the historic external market factor data.
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11. A computer system comprising:
a processor coupled with a data transaction processing system;
a non-transitory computer-readable medium coupled with the processor, the non-transitory computer-readable medium storing computer-executable instructions executable by the computer system to cause the processor to:
encode, using a plurality of first layers of a layered plurality of interconnected processing nodes of a structured neural network, historic participant transaction data for a participant in a data transaction processing system and historic external market factor data including data indicative of characteristics of a financial derivative product traded on an exchange for a time period that corresponds to the historic participant transaction data that occurs during the time period, wherein at least a subset of the interconnections of the layered plurality of interconnected processing nodes are dynamically weighted;
decode the encoded data using a plurality of second layers of the layered plurality of interconnected processing nodes of the structured neural network;
compare, by the processor, using the structured neural network, the decoded data with the historic participant transaction data and the historic external market factor data;
identify, using the structured neural network, one or more patterns in the layered plurality of interconnected processing nodes when the decoded data is within a predefined distance of the historic participant transaction data and the historic external market factor data;
receive data indicative of a new transaction;
generate a level of deviation based on a comparison of the data indicative of the new transaction and current external market factor data with the one or more patterns; and
taking an action when the level of deviation exceeds a threshold.
12. The computer system of claim 11, wherein the action includes:
generate an alert when the level of deviation exceeds the threshold.
1. A computer implemented method comprising:
identifying, by a processor coupled with a data transaction processing system, using a structured neural network comprising a layered plurality of interconnected processing nodes, one or more patterns in historic participant transaction data for a participant in the data transaction processing system and historic external market factor data including data indicative of characteristics of a financial derivative product traded on an exchange for a time period that corresponds to the historic participant transaction data that occurs during the time period, the one or more patterns indicative of a historical normal activity by the participant in relation to the historic external market factor data, wherein at least a subset of the interconnections of the layered plurality of interconnected processing nodes are dynamically weighted;
receiving, by the processor, from the participant, data indicative of a new transaction;
calculating, by the processor, current external market factor data;
comparing, by the processor, the data indicative of the new transaction and the current external market factor data with the one or more patterns; generating, by the processor, an abnormality score for the new transaction based on the comparison; and
generating, by the processor, an alert when the abnormality score exceeds a first threshold.
2. The computer implemented method of claim 1, wherein identifying the one or more patterns comprises:
encoding, by the processor, using the structured neural network, the historic participant transaction data and the historic external market factor data using a plurality of first layers of the layered plurality of interconnected processing nodes; decoding, by the processor, using the structured neural network, the encoded data using a plurality of second layers of the layered plurality of interconnected processing nodes;
comparing, by the processor, using the structured neural network, the decoded data with the historic participant transaction data and the historic external market factor data; and identifying, by the processor, using the structured neural network, the one or more patterns in the layered plurality of interconnected processing nodes when the decoded data is within a predefined distance of the historic participant transaction data and the historic external market factor data.
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21. A computer system comprising:
means for encoding, using a plurality of first layers of a layered plurality of interconnected processing nodes of a structured neural network, historic participant transaction data for a participant in a data transaction processing system and historic external market factor data including data indicative of characteristics of a financial derivative product traded on an exchange for a time period that corresponds to the historic participant transaction data that occurs during the time period, wherein at least a subset of the interconnections of the layered plurality of interconnected processing nodes are dynamically weighted;
means for decoding the encoded data using a plurality of second layers of the layered plurality of interconnected processing nodes of the structured neural network;
means for comparing, using the structured neural network, the decoded data with the historic participant transaction data and the historic external market factor data;
means for identifying, using the structured neural network, one or more patterns in the layered plurality of interconnected processing nodes when the decoded data is within a predefined distance of the historic participant transaction data and the historic external market factor data;
means for receiving data indicative of a new transaction;
means for generating a level of deviation by comparing the new transaction and current external market factor data with the one or more patterns; and
means for taking an action, when the level of deviation exceeds a threshold.
21. A computer system comprising:
means for identifying, using a structured neural network comprising a layered plurality of interconnected processing nodes, one or more patterns in historic participant transaction data for a participant in a data transaction processing system and historic external market factor data including data indicative of characteristics of a financial derivative product traded on an exchange for a time period that corresponds to the historic participant transaction data that occurs during the time period, the one or more patterns indicative of historical normal activity by the participant in relation to the historic external market factor data, wherein at least a subset of the interconnections of the layered plurality of interconnected processing nodes are dynamically weighted;
means for receiving from the participant, data indicative of a new transaction;
means for calculating current external market factor data; means for comparing the data indicative of the new transaction and the current external market factor data with the one or more patterns;
means for generating an abnormality score for the new transaction based on the comparison; and
means for generating an alert when the abnormality score exceeds a first threshold.
Status of Prior Art
The closest prior art, Moritz, Kim, and Zhang, do not teach “encoding, by a processor coupled with a data transaction processing system, using a plurality of first layers of a layered plurality of interconnected processing nodes of a structured neural network, historic participant transaction data for a participant in the data transaction processing system and historic external market factor data including data indicative of characteristics of a financial derivative product traded on an exchange for a time period that corresponds to the historic participant transaction data that occurs during the time period, wherein at least a subset of the interconnections of the layered plurality of interconnected processing nodes are dynamically weighted.” Kim considers only the change of stock market data itself; the data used in the study in section 4.1 was the daily closing price of 781 companies. The closing price is not transaction or participant data and, therefore, Kim does not teach the claimed features.
Conclusion
Claims 1-21 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL T PELLETT whose telephone number is (571)270-7156. The examiner can normally be reached on Monday - Friday 9-5 EST.
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/DANIEL T PELLETT/Primary Examiner, Art Unit 2121
