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
The instant application having Application No. 17337140 has a total of 24 claims pending in the application, of which claims 4, 10, 15, and 21 have been cancelled.
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.
Claim 1-9, 11-14, 16-20 and 22-24 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of U.S. Patent No.12619918 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because each of the limitations of claims 1-9, 11-14, 16-20 and 22-24 can be met by the claims of 12619918 B2.
Instant Application
12619918 B2
Examiners Comment
An apparatus comprising
Claim 1: An apparatus, comprising:
A memory storing instructions
Claim 1: a memory storing instructions
A communications interface;
Claim 1: a communications interface
At least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to:
Claim 1: At least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to:
Receive an identifier of a customer from a computing system via the communications interface, and based on the identifier, obtain, from the memory, elements of first interaction data that characterize the customer during an extraction interval associated with a trained artificial intelligence process
Claim 1: Perform operations that train an artificial intelligence process… an identifier associated with a customer from a computing system, and based on the received identifier, obtain, from the memory, first elements of consolidated data associated with a first temporal interval and with the received identifier
Generate an input dataset for the trained artificial intelligence process based on elements of interaction data associated with the identifier and the extraction interval
Claim 1: Perform operations that train an artificial intelligence process… Generate a first input dataset based on elements of first interaction data associated with a first temporal interval
Both references deal with time based data, here the temporal interval is the same as the extraction interval as both are over time.
Perform operations that apply a trained artificial intelligence process to the input dataset, and based on the application of the trained artificial intelligence process to the input dataset, generate output data comprising a numerical score representative of a predicted likelihood of (i) a non-occurrence of a first event during a first portion of a target interval and (ii) an occurrence of the first event during a second portion of the target interval, the target interval being subsequent to the extraction interval, the second portion of the target interval being separated from the extraction interval by the first portion of the target interval, and the occurrence of the first event being associated with a temporal duration that exceeds a threshold temporal duration within the second portion of the target interval;
Claim 1: based on an application of a trained first artificial intelligence process to the first input dataset, generate output data representative of a predicted likelihood of an occurrence of each of a plurality of target events during a second temporal interval, the second temporal interval being subsequent to the first temporal interval and being separated from the first temporal interval by a corresponding buffer interval
Claim 2: a numerical score indicative of the predicted likelihood
The use of multiple events vs a single event, and different references to various time intervals within the target intervals are obvious variations of the same idea. The only differences here are that the first interval comes before the second in the reference application, there’s a little more detail as to the timing of events, and the use of a buffer interval. As this is all dealing with time, having a gap of time between events would be something common when looking at different time points.
Transmit the identifier and at least a portion of the generated output data to the computing system via the communications interface, the computing system being configured to perform operations that obtain, from a data repository, second interaction data associated with the customer and the first event based on the identifier, that apply one or more treatment processes associated with the customer and the first event to the second interaction data, and that, based on the application of the one or more treatment processes to the second interaction data, modify the second interaction data in accordance with the portion of the output data, the modification to the second interaction data reducing the predicted likelihood of the occurrence of the first event during the second portion of the target interval .
Claim 1: transmit at least a portion of the output data to a computing system via the communications interface, the computing system being configured to generate, based on the portion of the output data, notification data associated with the predicted likelihood of the occurrence of at least one of the target events and to provision the notification data to a device.
Here the operations would be providing a notification to the device, with the notification being the proposed modification, with the treatment aspects to solve the predicted likelihood of the target event being obvious to one of ordinary skill in the art of finance decisions at the time of filing, with the motivation to solve or otherwise provide a solution to any negative target events that can be used to maintain profitability of the customer.
As can be shown, each limitation of the instant application is met by claims 1-13 of U.S. Patent No.12619918 B2, and therefore the claim is rejected under obvious type double patenting.
As per claims 2-9, 11-14, 16-20 and 22-24, these claims are rejected for similar reasons to claim 1 over claims 1-13 of U.S. Patent No.12619918 B2.
Double Patenting
Claims 1-9 and 11-20 and 22 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11 of copending Application No. 17726184 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because each of the limitations of claims 1-20 can be met by the claims of 17528362.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application
17726184
Examiners Comment
An apparatus comprising
Claim 1: An apparatus, comprising:
A memory storing instructions
Claim 1: a memory storing instructions
A communications interface;
Claim 1: a communications interface
At least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to:
Claim 1: At least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to:
Receive an identifier of a customer from a computing system via the communications interface, and based on the identifier, obtain, from the memory, elements of first interaction data that characterize the customer during an extraction interval associated with a trained artificial intelligence process
Claim 1: receive, via the communications interface, a first identifier associated with a customer from a computing system, and based on the first identifier, obtain elements of first interaction data associated with the customer form a portion of a data repository … for a trained artificial intelligence process
Generate an input dataset for the trained artificial intelligence process based on elements of interaction data associated with the identifier and the extraction interval
Claim 1: generate an input dataset for a trained artificial intelligence process based on elements of first interaction data … the elements of first interaction data characterizing an occurrence of a first event during a first temporal interval
Both references deal with time based data, here the temporal interval is the same as the extraction interval as both are over time.
Perform operations that apply a trained artificial intelligence process to the input dataset, and based on the application of the trained artificial intelligence process to the input dataset, generate output data comprising a numerical score representative of a predicted likelihood of (i) a non-occurrence of a first event during a first portion of a target interval and (ii) an occurrence of the first event during a second portion of the target interval, the target interval being subsequent to the extraction interval, the second portion of the target interval being separated from the extraction interval by the first portion of the target interval, and the occurrence of the first event being associated with a temporal duration that exceeds a threshold temporal duration within the second portion of the target interval;
Claim 1: apply the trained artificial intelligence process to the input dataset, and based on the application of the trained artificial intelligence process to the input dataset, generate output data comprising a numerical value representative of a predicted likelihood of an occurrence of a second event during a second temporal interval, the second event being associated with the first event, and the second temporal interval being subsequent to the first temporal interval and being separated from the first temporal interval by a corresponding buffer interval
The use of multiple events vs a single event, and different references to various time intervals within the target intervals are obvious variations of the same idea. The only differences here are that the first interval comes before the second in the reference application, discussions of when particular events happen within the time period and the use of a buffer interval. As this is all dealing with time, having a gap of time between events would be something common when looking at different time points.
Transmit the identifier and at least a portion of the generated output data to the computing system via the communications interface, the computing system being configured to perform operations that obtain, from a data repository, second interaction data associated with the customer and the first event based on the identifier, that apply one or more treatment processes associated with the customer and the first event to the second interaction data, and that, based on the application of the one or more treatment processes to the second interaction data, modify the second interaction data in accordance with the portion of the output data, the modification to the second interaction data reducing the predicted likelihood of the occurrence of the first event during the second portion of the target interval .
Claim 1: transmit… at least a portion of the output data to a computing system via the communications interface, the computing system being configured to perform one or more operations associated with the first identifier and in accordance with the portion of the output data, and the one or more operations being associated with a reduction in the predicted likelihood of the occurrence of the second event during the second temporal interval.
As can be shown, each limitation of the instant application is met by reference application 17726184, and therefore the claim is provisionally rejected under obvious type double patenting.
As per claims 2-9, 11-14, 16-20 and 22-24, these claims are rejected for similar reasons to claim 1 over claims 1-11 of the reference application 17726184.
Double Patenting
Claims 1-9, 11-14, 16-20 and 22-24 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-12 of copending Application No. 17681215 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because each of the limitations of claims 1-20 can be met by the claims of 17681215.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application
17681215
Examiners Comment
An apparatus comprising
Claim 1: An apparatus, comprising:
A memory storing instructions
Claim 1: a memory storing instructions
A communications interface;
Claim 1: a communications interface
At least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to:
Claim 1: At least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to:
Receive an identifier of a customer from a computing system via the communications interface, and based on the identifier, obtain, from the memory, elements of first interaction data that characterize the customer during an extraction interval associated with a trained artificial intelligence process
Claim 1: … train an artificial intelligence process…
Claim 6: wherein the first interaction data comprises a customer identifier associated with a customer and a temporal identifier associated with the first temporal interval
Generate an input dataset for the trained artificial intelligence process based on elements of interaction data associated with the identifier and the extraction interval
Claim 1: … train an artificial intelligence process… generate an input dataset based on elements of first interaction data associated with a first temporal interval
Both references deal with time based data, here the temporal interval is the same as the extraction interval as both are over time.
Perform operations that apply a trained artificial intelligence process to the input dataset, and based on the application of the trained artificial intelligence process to the input dataset, generate output data comprising a numerical score representative of a predicted likelihood of (i) a non-occurrence of a first event during a first portion of a target interval and (ii) an occurrence of the first event during a second portion of the target interval, the target interval being subsequent to the extraction interval, the second portion of the target interval being separated from the extraction interval by the first portion of the target interval, and the occurrence of the first event being associated with a temporal duration that exceeds a threshold temporal duration within the second portion of the target interval;
Claim 1: apply the trained artificial intelligence process to the input dataset, and based on the application of the trained artificial intelligence process to the input dataset, generate output data comprising numerical values indicative of a (i) predicted likelihood of an occurrence of each of a plurality of targeted events during a second temporal interval, the second temporal interval being subsequent to the first temporal interval and being separated from the first temporal interval by a corresponding buffer interval;
The use of multiple events vs a single event, and different references to various time intervals within the target intervals are obvious variations of the same idea. The only differences here are that the first interval comes before the second in the reference application, various descriptions of where events occur within the time period and the use of a buffer interval. As this is all dealing with time, having a gap of time between events would be something common when looking at different time points.
Transmit the identifier and at least a portion of the generated output data to the computing system via the communications interface, the computing system being configured to perform operations that obtain, from a data repository, second interaction data associated with the customer and the first event based on the identifier, that apply one or more treatment processes associated with the customer and the first event to the second interaction data, and that, based on the application of the one or more treatment processes to the second interaction data, modify the second interaction data in accordance with the portion of the output data, the modification to the second interaction data reducing the predicted likelihood of the occurrence of the first event during the second portion of the target interval .
Claim 1: transmit the output data to a computing system via the communications interface, the computing system being configured to transmit digital content to the device based on at least a portion of the output data.
Here the digital content would be an example of potential operations one could send in response to making predictions about a user including modifications to data for the system.
As can be shown, each limitation of the instant application is met by reference application 17681215, and therefore the claim is provisionally rejected under obvious type double patenting.
As per claims 2-9, 11-14, 16-20 and 22-24, these claims are rejected for similar reasons to claim 1 over claims 1-12 of the reference application 17681215.
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 1-9, 11-14, 16-20 and 22-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a machine type claim. Claim 12 is a process type claim. Claim 20 is a manufacture type claim. Therefore, claims 1-9, 11-14, 16-20 and 22-24 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“Generate an input dataset… based on the elements of first interaction data associated with the identifier and the extraction interval” A user mentally or with pencil and paper assembles the interaction data based on the identifier and the associated extraction interval.
“perform operations that apply a … process to the input dataset, and based on the application of the … process to the input dataset, generate output data comprising a numerical score representative of a predicted likelihood of (i) a non-occurrence of a first event during a first portion of a target interval, and (ii) an occurrence of the first event during a second portion of the target interval, the target interval being subsequent to the extraction interval, the second portion of the target interval being separated from the extraction interval by the first portion of the target interval, and the occurrence of the first event being associated with a temporal duration that exceeds a threshold temporal duration within the second portion of the target interval” The user mentally or with pencil and paper uses a process to look at previous information and then make a prediction about future events comprising some sort of numerical score to represent the prediction.
“that apply one or more treatment processes associated with the customer and the first event to the second interaction data, and that based on the application of the one or more treatment processes to the second interaction data, modify the second interaction data in accordance with the portion of the output data, the modification to the second interaction data reducing the predicted likelihood of the occurrence of the first event during the second portion of the target interval” The user, mentally or with pencil and paper, takes an action based upon the prediction they made in order to make the occurrence of the event less likely to occur.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“A memory”, “a communications interface”, “at least one processor”, “the memory”, , “the communications interface”, “the at least one processor”, “a computing system”, “the computing system” (mere instructions to apply the exception using a generic computer component);
“a trained artificial intelligence process”, “the trained artificial intelligence process” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: The machine learning here is generic, off the shelf machine learning. Any training is inherently required in the use of a machine learning algorithm, and this claim has no detail or aspects which move this machine learning model from a generic, off the shelf machine learning model).
“receive the identifier of a customer from a computing system via the communications interface and based on the identifier, obtain, from the memory, elements of first interaction data that characterize the customer during an extraction temporal interval associated with a trained artificial intelligence process”, “transmit the identifier and at least a portion of the generated output data to the computing system via the communications interface, the computing system being configured to perform operations that obtain, from a data repository, second interaction data associated with the customer and the first event based on the identifier…” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“A memory”, “a communications interface”, “at least one processor”, “the memory”, , “the communications interface”, “the at least one processor”, “a computing system”, “the computing system” (mere instructions to apply the exception using a generic computer component)
“a trained artificial intelligence process”, “the trained artificial intelligence process” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: The machine learning here is generic, off the shelf machine learning. Any training is inherently required in the use of a machine learning algorithm, and this claim has no detail or aspects which move this machine learning model from a generic, off the shelf machine learning model).
“receive the identifier of a customer from a computing system via the communications interface and based on the identifier, obtain, from the memory, elements of first interaction data that characterize the customer during an extraction temporal interval associated with a trained artificial intelligence process”, “transmit the identifier and at least a portion of the generated output data to the computing system via the communications interface, the computing system being configured to perform operations that obtain, from a data repository, second interaction data associated with the customer and the first event based on the identifier…” (MPEP 2106.05(d)(II) indicate that merely “receiving or transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed transmitting step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 2-3, 6-8, these claims contain additional generic machine learning aspects, and mental steps of determining, examining the data, and responding to the data and is rejected similarly to claim 1.
As per claims 9, 11 and 23, these claims contain additional mental steps of making a prediction and dealing with incoming data, and is rejected similarly to claim 1 above.
As per claims 5 and 24, these claims denote additional generic machine learning aspects and mental steps, and is rejected similarly to claim 1 above.
As per claim 22, this claim contains additional generic computer hardware and mental steps to claim 1, and is rejected for similar reasons to claim 1.
As per claim 12,
2A Prong 1:
“generating … an input dataset … based on the elements of first interaction data associated with the identifier and the extraction interval” A user mentally or with pencil and paper assembles the interaction data based on the identifier and the associated extraction interval.
“Performing operations … that apply a … process to the input dataset … based on the application of the … process to the input dataset, generate output data comprising a numerical score representative of a predicted likelihood of an occurrence (i) a non-occurrence of a first event during a first portion of a target interval and (ii) an occurrence of the first even during a second portion of the target interval, the target interval being subsequent to the extraction interval, the second portion of the target interval being separated from the extraction interval by the first portion of the target interval, and the occurrence of the first event being associated with a temporal duration that exceeds a threshold temporal duration within the second portion of the target interval” The user mentally or with pencil and paper uses a process to look at previous information and then make a prediction about future events comprising some form of numerical value.
“… that apply one or more treatment processes associated with the customer and the first event to the second interaction data, and that based on the application of the one or more treatment processes to the second interaction data, modify the second interaction data in accordance with the portion of the output data, the modification to the second interaction data reducing the predicted likelihood of the occurrence of the first event during the second portion of the target interval ” The user, mentally or with pencil and paper, takes an action based upon the prediction they made to reduce the likelihood of the event occurring in the second portion of the timeframe.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“A computer”, “at least one processor”, “the at least one processor”, “a computing system”, “the computing system” (mere instructions to apply the exception using a generic computer component);
“a trained artificial intelligence process”, “the trained artificial intelligence process” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: The machine learning here is generic, off the shelf machine learning. Any training is inherently required in the use of a machine learning algorithm, and this claim has no detail or aspects which move this machine learning model from a generic, off the shelf machine learning model).
“receiving an identifier of a customer from a computing system using the at least one processor and based on the identifier, obtaining, from a data repository using the at least one processor, elements of first interaction data that characterize the customer during an extraction interval associated with a trained artificial intelligence process”, “transmitting…the identifier and at least a portion of the generated output data to a computing system via the communications interface, the computing system being configured to perform operations that obtain, from an additional data repository, second interaction data associated with the customer and the first event based on the identifier…” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“A computer”, “at least one processor”, “the at least one processor”, “a computing system”, “the computing system” (mere instructions to apply the exception using a generic computer component)
“a trained artificial intelligence process”, “the trained artificial intelligence process” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: The machine learning here is generic, off the shelf machine learning. Any training is inherently required in the use of a machine learning algorithm, and this claim has no detail or aspects which move this machine learning model from a generic, off the shelf machine learning model).
“receiving an identifier of a customer from a computing system using the at least one processor and based on the identifier, obtaining, from a data repository using the at least one processor, elements of first interaction data that characterize the customer during an extraction interval associated with a trained artificial intelligence process”, “transmitting…the identifier and at least a portion of the generated output data to a computing system via the communications interface, the computing system being configured to perform operations that obtain, from an additional data repository, second interaction data associated with the customer and the first event based on the identifier…” (MPEP 2106.05(d)(II) indicate that merely “transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed transmitting step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 13-14, and 17-18, these claims contain additional generic machine learning aspects, and mental steps of determining, examining the data, and responding to the data and is rejected similarly to claim 12.
As per claim 19, this contains additional mental steps of making a prediction and dealing with incoming data, and is rejected similarly to claim 12 above.
As per claim 16, this denotes additional generic machine learning aspects and mental steps, and is rejected similarly to claim 12 above.
As per claim 20,
2A Prong 1:
“generating an input dataset… based on the elements of first interaction data associated with the identifier and the extraction interval” A user mentally or with pencil and paper assembles the interaction data based on the identifier and the associated extraction interval.
“performing operations that apply a … process to the input dataset and based on an application of the … process to the input dataset, that generate output data comprising a numerical score representative of a predicted likelihood of (i) a non-occurrence of a first event during a first portion of a target interval and (ii) an occurrence of the first event during a second portion of the target interval, the target interval being subsequent to the extraction interval, the second portion of the target interval being separated from the extraction interval by the first portion of the target interval, and the occurrence of the first event being associated with a temporal duration within the second portion of the target interval” The user mentally or with pencil and paper uses a process to look at previous information and then make a prediction about future events.
“… that apply one or more treatment processes associated with the customer and the first event to the second interaction data, and that based on the application of the one or more treatment processes to the second interaction data, modify the second interaction data in accordance with the portion of the output data, the modification to the second interaction data reducing the predicted likelihood of the occurrence of the first event during the second portion of the target interval” The user, mentally or with pencil and paper, takes an action based upon the prediction they made to reduce the likelihood of the event in the second portion of the timeframe.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“tangible non-transitory compute readable medium“, “at least one processor”, “the at least one processor”, “a computing system”, “the computing system” (mere instructions to apply the exception using a generic computer component);
“a trained artificial intelligence process”, “the trained artificial intelligence process” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: The machine learning here is generic, off the shelf machine learning. Any training is inherently required in the use of a machine learning algorithm, and this claim has no detail or aspects which move this machine learning model from a generic, off the shelf machine learning model).
“receiving an identifier of a customer from a computing system, and based on the identifier, obtaining, from a data repository, elements of first interaction data that characterize the customer during an extraction interval associated with a trained artificial intelligence process”, “transmitting the identifier and at least a portion of the generated output data to a computing system via the communications interface, the computing system being configured to perform operations that obtain, from an additional data repository, second interaction data associated with the customer and the first event based on the identifiers, and… ” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“tangible non-transitory compute readable medium“, “at least one processor”, “the at least one processor”, “a computing system”, “the computing system” (mere instructions to apply the exception using a generic computer component)
“a trained artificial intelligence process”, “the trained artificial intelligence process” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: The machine learning here is generic, off the shelf machine learning. Any training is inherently required in the use of a machine learning algorithm, and this claim has no detail or aspects which move this machine learning model from a generic, off the shelf machine learning model).
““receiving an identifier of a customer from a computing system, and based on the identifier, obtaining, from a data repository, elements of first interaction data that characterize the customer during an extraction interval associated with a trained artificial intelligence process”, “transmitting the identifier and at least a portion of the generated output data to a computing system via the communications interface, the computing system being configured to perform operations that obtain, from an additional data repository, second interaction data associated with the customer and the first event based on the identifiers, and… ” (MPEP 2106.05(d)(II) indicate that merely “transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed transmitting step is well-understood, routine, conventional activity is supported under Berkheimer).
Allowable Subject Matter
Independent claims 1, 12, and 20, and their respective dependent claims (2-3, 6-9, 11-14, 16-20, and 22-24) are considered allowable if the rejection under U.S.C. 101 can be overcome, since when reading the claims in light of the specification, as per MPEP $2111.01 none of the references of record alone or in combination disclose or suggest the combination of limitations found within the independent claims. While the individual concepts can be found in light of the Dorai, Guy, Zeng, and Lawrence references as shown in the previous office action, with the treatment options being found in the Dorai reference (see pg.5, paragraphs 0070), and the splitting of responsibilities between multiple locations described by the Lawrence reference, the combination of the above limitations and the taking of specific actions in specific locations would not be obvious to one of ordinary skill in the art at the time of filing, and therefore the claims would be found allowable over the prior art if the rejection under U.S.C. 101 were overcome.
Response to Arguments
In pg.23-24, the Applicant argues in regards to the rejection under U.S.C. 101,
As an initial point, Applicant submits that the Office's summary of the elements recited by Applicant's independent claims mischaracterizes the actual language recited by the claims and is inconsistent with the Office's current examination processes. Indeed, Applicant's claims do not recite "applying a process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of a first event during a first portion of a target interval," as alleged by the Office- instead, Applicant's independent claims, in unamended form, "perform operations that apply a trained artificial intelligence process to the input dataset, and based on the application of the trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of (i) a non-occurrence of a first event during a first portion of a target interval and (ii) an occurrence of the first event during a second portion of the target interval, the target interval being subsequent to the extraction interval." Id. (emphases added). The Office's apparent abstraction of the "application of a trained artificial- intelligence process" from Applicant's independent claims during its analysis under Prong One of Revised Step 2A of the Alice/Mayo finds no support within the Office's current examination processes, and Applicant submits that the abstraction of the "application of a trained artificial- intelligence process," which is not a mental process, from Applicant's independent claims is plainly inconsistent with these procedures, which require the Office to "[i]dentify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea." 2019 Guidance, 84 Fed. Reg. 4, p. 54. As the Office's analysis under Prong One of Revised Step 2A of the Alice/Mayo fails to rely on the specific limitations of Applicant's claims, the Office's analysis is inconsistent with the Office's current examination procedures and the rejection of Applicant's claims under 35 U.S.C. §101 is improper and should be withdrawn.
In response, the Examiner maintains the rejection as shown above and in the previous office action. At no point has the Examiner stated that use of a trained artificial intelligence process has been part of prong one. The machine learning/artificial intelligence aspects are a part of Prong 2. The Applicant is correct that use of machine learning algorithm/artificial intelligence process is not a mental process, it is part of the “apply it” aspects of taking an abstract idea and using generic computer equipment or machine learning models to implement or apply the abstract idea. As there has been no attempt to call the artificial intelligence process as part of Prong 1, the rejection is maintained as shown above.
In pg.27, the Applicant further argues in regards to the rejection under U.S.C. 101,
Furthermore, and contrary to the Office's assertions, the elements recited by Applicant's independent claims, when considered as a whole even in unamended form, provide a specific, technological improvement to existing, computer-implemented predictive processes that ingest, operate on, and process increasingly large volumes of interaction data as such, integrate any allegedly recited abstract idea into a patent-eligible, practical application. See, e.g., Applicant's Amendment filed September 15, 2025, pp. 20-27 (citing Applicant's Specification, ПП [0020]-[0023]).
In response, the Examiner maintains the rejection as shown above. The Applicant has at no time even described what technology is being improved. The ability to process large volumes of “interaction data” is not an improvement to a technology. A generic computer processor is capable of implementing far more calculations in a shorter time than a human ever could, but putting an abstract idea onto a processor is not an improvement to the processor, it is an improvement to he abstract idea. Paragraphs 0020-0023 merely describe providing customer specific events in order to analyze credit products, missed payments, and other financial situations, all of which have been performed by human beings for hundreds if not thousands of years. Merely placing them onto a generic computer and using a generic machine learning model does not change this from an abstract idea capable of being performed in the human mind and/or with pencil and paper, and therefore the rejection is maintained as shown above.
In pg.28, Applicant further argues in regards to the rejection under U.S.C. 101,
These quoted elements recited similarly of Applicant's amended independent claims when considered individually and as a whole in accordance with the Office's current examination procedures, represent a specific, technological improvement to computing systems and environments that implement existing, computer-implemented predictive processes, which often an iterative application of machine learning or artificial processes to corresponding sets of input data in an effort to predict and characterize future occurrences and non-occurrences of events during target temporal intervals. See, e.g., Applicant's Specification, ПП [0019]-[0023]. Indeed, by performing operations that, through a single application of a specially trained artificial intelligence process to an input dataset derived from interaction data associated with a customer identifier, dynamically generate the output data indicating a predicted likelihood of both a non- occurrence of a first event during a first portion of a target interval and an occurrence of the first event during a second portion of the target interval in real-time and upon receipt of the customer identifier, the specific, technological solution provided by Applicant's independent claims, and described in Applicant's Specification, reduces a number of discrete computational operations, and as such, an amount of computational resources, required to generate the claimed output data when compared to the existing, computer-implemented predictive solutions described in Applicant's Specification, which require an iterative application of machine learning or artificial processes to corresponding sets of input data in order to obtain output data indicative of the non-occurrence of a first event during a first portion of a target interval and the occurrence of the first event during a second portion of the target interval. See, e.g., id.
In response, the Examiner maintains the rejection as shown above. Applicant claims that reducing the number of computational operations needed when interacting with the machine learning process through the limitations of their claim. However, none of these details disclose an improvement or limitation changing the machine learning algorithm, they all deal with the information going into or coming out of the algorithm, with the algorithm being a generic, “trained artificial intelligence process.” This is nothing more than a generic, off the shelf artificial intelligence algorithm, and it is treated no differently than a processor or memory being used to implement an abstract idea. Putting more efficient data into a processor or into storage in memory is not an improvement to the memory or the processor, and changing the data going into/coming out of an algorithm is not an improvement to the algorithm. This is an improvement to the abstract idea, and therefore not significantly more than the abstract idea, and the rejection is maintained under U.S.C. 101.
In pg.29, the Applicant further argues in regards to the rejection under U.S.C. 101,
Furthermore, and contrary to the Office's assertions, Applicant submits that the claimed "artificial intelligence process" recited in Applicant's independent claims and described in Applicant's Specification extend beyond any alleged "generic, off the shelf machine learning." See Office Action, p. 19. Instead, Applicant's Specification described, and Applicant's independent claims recite, an "artificial intelligence process" trained specifically to predict a likelihood of both a non-occurrence of a first event during a first portion of a target interval and an occurrence of the first event during a second portion of a target interval using customer-specific training datasets drawn from pre-processed and consolidated elements of customer-specific interaction data associated with corresponding training interval, using elements of ground truth data indicative of whether each of the customer-specific training datasets is associated with the non-occurrence of a first event during a first portion of the corresponding training temporal interval and with the occurrence of the first event during a second portion of the corresponding training interval, and using customer-specific validation datasets drawn from pre- processed and consolidated elements of customer-specific interaction data associated with corresponding validation intervals. See Applicant's Specification, " [0019]-[0023] and [0078]-[0108]. For these reasons, the "trained artificial intelligence process" recited by Applicant's independent claims, and described in Applicant's Specification, extends beyond a mere application of an existing artificial-intelligence process to a new field of use, and provides a specific improvement to any alleged "generic, off the shelf machine learning" and to the existing predictive processes described in Applicant's Specification, which apply trained predictive processes to input datasets in response to a detected occurrence of an event and not prior to the occurrence of that event. See, e.g., id., at TT [0019]-[0023]. Indeed, in Ex parte Desjardins, Director Squires cautions explicitly that "Examiners should not evaluate claims at a high level of generality" within Prong Two of Step 2A of the Alice/Mayo test, as such a cursory analysis without adequate explanation may render "many AI innovations potentially unpatentable[,] even if they are adequately described and non-obvious." Ex parte Desjardins, p. 9 (emphases added).
In response, the Examiner maintains the rejection as shown above. Once again the Applicant quotes large portions of their claim set and claims that this causes the claim to be more than generic machine learning. However, none of these limitations amount to anything more than the data going into and data coming out of the generic machine learning model. Once again, changing the data going into a model or into a processor does not improve the model or the processor. Since there is no improvement to the machine learning model, this is not significantly more than the abstract idea, and therefore the rejection is maintained as shown above.
In pg.31, the Applicant argues in regards to the Double patenting rejections,
Here, based on a tabular comparison between the claims of the instant application that the reference application without comment or analysis, the Office concludes that the "although the claims at issue are not identical, they are not patentably distinct from each other because each of the limitations of [Applicant's claims] can be met by the claims of [the reference applications]." Office Action, p. 3-16. The Office's conclusory analysis of Applicant's claims is plainly inconsistent with the Office's own examination procedures and as such, a prima facie basis for the double patenting rejection of Applicant's claims cannot be established, even in unamended form. See M.P.E.P. § 804(II)(B)(2). For these reasons, the provisional double patenting rejection of these claims is improper and should be withdrawn.
In response, the Examiner maintains the rejection as shown above. Applicant makes no actual arguments, merely stating that the rejection is “inconsistent with the Office’s own examination procedures” but fails to point out any differences or make any arguments as to why the double patenting rejection does not meet the claims. Since these arguments are conclusory, the rejection is maintained as shown above.
Applicant's arguments with respect to claims 1-4, 6-9, 11-14, 16-20, and 22-24 have been considered but are either conclusory statements or are rejected for similar reasons given to those above.
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 BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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