DETAILED ACTION
This Office action is in reply to application no. 19/346,053, filed 30 September 2025. Claims 1-20 are pending and are considered below.
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 .
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 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,450,618. Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are broader than, but otherwise identical to, the claims of the reference patent, in two aspects. First, in the reference patent, training a machine learning program is explicitly within the scope of the claimed process, whereas in the present application it is an external step which happened before commencement of the claimed method. Second, the reference patent includes a step of predicting a fraud classification, which is more specific than the claimed generating of such a classification in the present claims. The other independent claims are simply different embodiments but are directed to computers performing the same process; the dependent claims are identical to, or patentably indistinct from, those of the reference patent.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims lie within statutory categories of invention, as each is directed to a method (process), system (machine) or non-transitory computer readable medium (manufacture). The claim(s) recite(s) creating a set of fraud classification rules, accessing account data associated with an account, and generating, in no particular manner, a fraud classification that identifies a particular type of fraud.
First, determining fraudulent use of an account is both a fundamental business practice and a commercial interaction, each of which is among the “certain methods of organizing human activity” deemed abstract. Second, in the absence of computers, these are steps that can be performed mentally and with paper records.
A banker could create fraud classification rules by writing them on a paper or by simply remembering them, could access account data by consulting paper records, and could select a particular type of fraud based on mental consideration of the available data. None of this presents any practical difficulty and none requires any technology beyond the use of a pen and paper.
This judicial exception is not integrated into a practical application because aside from the bare inclusion of a generic computer, nothing is done beyond what was set forth above, which does not go beyond using a generic computer as a tool to implement the abstract idea. See MPEP § 2106.05(f).
As the claims only manipulate data relating to fraud, they do not improve the “functioning of a computer” or of “any other technology or technical field”. See MPEP § 2106.05(a). They do not apply the abstract idea “with, or by use of a particular machine”, MPEP § 2106.05(b), as the below-cited Guidance is clear that a generic computer is not the particular machine envisioned.
They do not effect a “transformation or reduction of a particular article to a different state or thing”, MPEP § 2106.05(c). First, such data, being intangible, are not a particular article at all. Second, the claimed manipulation is neither transformative nor reductive; as the courts have pointed out, in the end, data are still data.
They do not apply the abstract idea “in some other meaningful way beyond generally linking [it] to a particular technological environment”, MPEP § 2106.05(e), as the lack of technical and algorithmic detail in the claims is so as not to go beyond such a general linkage.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional claim limitations, considered individually and as an ordered combination, are insufficient to elevate an otherwise-ineligible claim.
Claim 12, which has the most, includes a processor and memory storing instructions. These elements are recited at a high degree of generality and the specification does not meaningfully limit them, such that a generic computer will suffice.
It only performs generic computer functions of nondescriptly manipulating information and sharing information with persons and/or other devices. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea.
The type of information being manipulated does not impose meaningful limitations or render the idea less abstract. Referring to a software component as a “trained machine learning program” trained on various data is of no patentable significance, as no training of such a program is within the scope of any independent claim.
The claim limitations when considered as an ordered combination – a generic computer performing a chronological sequence of abstract steps – do nothing more than when they are analyzed individually. The other independent claims are simply different embodiments but are likewise directed to a generic computer performing, essentially, the same process.
The dependent claims further do not amount to significantly more than the abstract idea: Claims 2, 6, 9, 10, 13 and 18 are simply further descriptive of the type of information being manipulated. Claims 3-5, 8, 11, 14-16, 19 and 20 simply recite further, abstract manipulation of data.
Claim 7 recites retraining the machine learning program in no particular manner but merely using certain data. In light of Recentive1, this is not, per se, sufficient to elevate the claim to patent eligibility.
The claims are not patent eligible. For further guidance please see MPEP § 2106.03 – 2106.07(c) (formerly referred to as the “2019 Revised Patent Subject Matter Eligibility Guidance”, 84 Fed. Reg. 50, 55 (7 January 2019, revised October 2019)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5 and 7-20 are rejected under 35 U.S.C. 103 as being unpatentable over Adjaoute (U.S. Publication No. 2016/0086185) in view of Ben-Or et al. (U.S. Patent No. 9,294,497).
In-line citations are to Adjaoute.
With regard to Claim 1:
Adjaoute teaches: A computer-implemented method [0055; a method is “implemented with special algorithms executed by computer apparatus”] for classifying a type of fraud associated with an account based on account data and fraud classification rules, comprising:
generating, by a computing system comprising a processor, the fraud classification rules, [0125; “building a business rules predictive model”; 0141; this may be related to “fraud-suspicious classes”] wherein:
the fraud classification rules are generated using a trained machine learning program [0126; a “trained model” is used] trained based on a data set indicating types of fraud, of a set of different types of fraud, associated with transactions or accounts; [0141 as cited above; 0198; this may be related to a user’s specific account]
accessing, by the computing system, the account data associated with a particular account; [0201; particular transactions associated with the accountholder are analyzed] and
generating, by the computing system… a fraud classification that identifies a particular type of fraud, included in the set of different types of fraud, associated with the particular account. [0260; a “compromise, takeover, or suspicious activity of an accountholder’s account” may be identified]
Adjaoute does not explicitly teach determining a type of fraud by applying the fraud classification rules to the account data, but it is known in the art. Ben-Or teaches a risk management system [title] that uses machine learning. [Col. 3, line 43] It may apply rules to transactions associated with accounts to determine fraud. [Col. 9, lines 35-36, 46-49] Ben-Or and Adjaoute are analogous art as each is directed to electronic means for using machine learning to identify information associated with fraud in financial transactions.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Ben-Or with that of Adjaoute in order to better identify complex fraud schemes, as taught by Ben-Or; [Col. 1, line 14] further, it is simply a substitution of one known part for another with predictable results, simply using rules with account data as in Ben-Or rather than the technique of Adjaoute; the substitution produces no new and unexpected result.
With regard to Claim 2:
The computer-implemented method of claim 1, wherein:
the account data comprises transaction data associated with a transaction, and
the fraud classification, generated by applying the fraud classification rules, indicates the particular type of fraud associated with the transaction. [0201; 0260 as cited above in regard to claim 1]
This claim is not patentably distinct from claim 1. First, it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of data which imparts neither structure nor functionality to the claimed method. Second, as the data only “comprises” this item, it can include other information, and any further processing can be based entirely on the other information. The reference is provided for the purpose of compact prosecution.
With regard to Claim 3:
The computer-implemented method of claim 1, further comprising:
determining, by the computing system, and based on the account data, that a transaction associated with the particular account is likely to be fraudulent, [0231; “a warning is generated to indicate that the data record is likely fraudulent”] wherein:
the fraud classification rules are applied to generate the fraud classification in response to determining that the transaction is likely to be fraudulent, and
the fraud classification identifies the particular type of fraud associated with the transaction. [Ben-Or in combination with Adjaoute as cited above in regard to claim 1; this is simply a substitution of one known part for another with predictable results, simply performing steps for the reason cited herein rather than, or in addition to, the reasons cited in regard to claim 1]
With regard to Claim 4:
The computer-implemented method of claim 3, further comprising:
generating, by the computing system, and using the machine learning program, fraud detection rules,
wherein the computing system determines that the transaction is likely to be fraudulent based on the fraud detection rules. [id.]
With regard to Claim 5:
The computer-implemented method of claim 1, further comprising:
determining, by the computing system, that the particular account is likely to be associated with a broad class of fraud; and
identifying, by the computing system, and by applying the fraud classification rules, a narrower class of fraud, within the broad class of fraud, that is associated with the particular account,
wherein the particular type of fraud, indicated by the fraud classification, comprises the narrower class of fraud. [Table 5; “fraud” as opposed to “no fraud” reads on broad classification; 0260 as cited above in regard to claim 1; specific categories of fraud are identified]
With regard to Claim 7:
The computer-implemented method of claim 1, further comprising:
receiving, by the computing system, feedback indicating an accuracy of the fraud classification;
re-training, by the computing system, the machine learning program using the feedback; and
updating, by the computing system, the fraud classification rules based on the re-training of the machine learning program. [0094; accuracy is measured; 0218; the incremental building of the neural network based on updated records reads on this]
With regard to Claim 8:
The computer-implemented method of claim 1, wherein generating the fraud classification comprises:
determining, by the computing system, based on the fraud classification rules, a fraud classification score associated with the particular type of fraud and the particular account; and
determining, by the computing system, that the fraud classification score exceeds a threshold score. [0029; thresholds are used in the decision process; 0200; a score is compared to a threshold; it would have been obvious to one then of ordinary skill in the art to use these measures with the already-disclosed fraud rules and types, as it is simply a substitution of known data for other data with predictable results]
With regard to Claim 9:
The computer-implemented method of claim 1, wherein the set of different types of fraud comprises two or more of: counterfeiting, forgery, account takeover, lost card use, stolen card use, skimming, chargeback fraud, application fraud, or a lack of fraud. [0260; a takeover as cited above in regard to claim 1; table 5; no fraud as cited above in regard to claim 1]
This claim is not patentably distinct from claim 1, as it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of data but which imparts neither structure nor functionality to the claimed method. The reference is provided for the purpose of compact prosecution.
With regard to Claim 10:
The computer-implemented method of claim 1, wherein the data set used to train the machine learning program further comprises: at least one of online activity data or location data associated with account holders, and the account holders correspond to the transactions or accounts. [0200; “online payments” are used]
With regard to Claim 11:
The computer-implemented method of claim 10, further comprising:
accessing, by the computing system, at least one of particular online activity data or particular location data associated with a particular account holder of the particular account, [0200 as cited above in regard to claim 10]
wherein the fraud classification is generated by applying the fraud classification rules to the account data and to the at least one of particular online activity data or the particular location data. [it would have been obvious to one then of ordinary skill in the art to use this data as it is merely a substitution of one known datum for another with predictable results]
With regard to Claim 12:
Adjaoute teaches: A computing system configured to classify a type of fraud associated with an account based on account data and fraud classification rules, the computing system comprising:
a processor; [0144; “processor”] and
memory storing computer-executable instructions that, when executed by the processor, [0055; “special algorithms executed by computer apparatus and delivered to non-transitory storage mediums”] cause the computing system to:
generate the fraud classification rules using a trained machine learning program trained based on a data set indicating types of fraud, of a set of different types of fraud, associated with transactions or accounts; [0126; a “trained model” is used; 0125; “building a business rules predictive model”; 0141; this may be related to “fraud-suspicious classes”; 0198; this may be related to a user’s specific account]
access the account data associated with a particular account; [0201; particular transactions associated with the accountholder are analyzed] and
generate… a fraud classification that identifies a particular type of fraud, included in the set of different types of fraud, associated with the particular account. [0260; a “compromise, takeover, or suspicious activity of an accountholder’s account” may be identified]
Adjaoute does not explicitly teach determining a type of fraud by applying the fraud classification rules to the account data, but it is known in the art. Ben-Or teaches a risk management system [title] that uses machine learning. [Col. 3, line 43] It may apply rules to transactions associated with accounts to determine fraud. [Col. 9, lines 35-36, 46-49] Ben-Or and Adjaoute are analogous art as each is directed to electronic means for using machine learning to identify information associated with fraud in financial transactions.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Ben-Or with that of Adjaoute in order to better identify complex fraud schemes, as taught by Ben-Or; [Col. 1, line 14] further, it is simply a substitution of one known part for another with predictable results, simply using rules with account data as in Ben-Or rather than the technique of Adjaoute; the substitution produces no new and unexpected result.
With regard to Claim 13:
The computing system of claim 12, wherein:
the account data comprises transaction data associated with a transaction, and
the fraud classification, generated by applying the fraud classification rules, indicates the particular type of fraud associated with the transaction. [0201; 0260 as cited above in regard to claim 12]
This claim is not patentably distinct from claim 12. First, it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of data which imparts neither structure nor functionality to the claimed system. Second, as the data only “comprises” this item, it can include other information, and any further processing can be based entirely on the other information. The reference is provided for the purpose of compact prosecution.
With regard to Claim 14:
The computing system of claim 12, wherein:
the computer-executable instructions further cause the computing system to determine, based on the account data, that a transaction associated with the particular account is likely to be fraudulent, [0231; “a warning is generated to indicate that the data record is likely fraudulent”]
the fraud classification rules are applied to generate the fraud classification in response to determining that the transaction is likely to be fraudulent, and
the fraud classification identifies the particular type of fraud associated with the transaction. [Ben-Or in combination with Adjaoute as cited above in regard to claim 12; this is simply a substitution of one known part for another with predictable results, simply performing steps for the reason cited herein rather than, or in addition to, the reasons cited in regard to claim 12]
With regard to Claim 15:
The computing system of claim 12, wherein:
the computer-executable instructions further cause the computing system to:
determine that the particular account is likely to be associated with a broad class of fraud; and
identify, by applying the fraud classification rules, a narrower class of fraud, within the broad class of fraud, that is associated with the particular account, and
the particular type of fraud, indicated by the fraud classification, comprises the narrower class of fraud. [Table 5; “fraud” as opposed to “no fraud” reads on broad classification; 0260 as cited above in regard to claim 12; specific categories of fraud are identified]
With regard to Claim 16:
The computing system of claim 12, wherein the computer-executable instructions further cause the computing system to:
receive feedback indicating an accuracy of the fraud classification;
re-train the machine learning program using the feedback; and
update the fraud classification rules based on re-training of the machine learning program. [0094; accuracy is measured; 0218; the incremental building of the neural network based on updated records reads on this]
With regard to Claim 17:
Adjaoute teaches: One or more non-transitory computer-readable media storing computer-executable instructions for classifying a type of fraud associated with an account based on account data and fraud classification rules, wherein the computer-executable instructions, when executed by a processor, [0055; “special algorithms executed by computer apparatus and delivered to non-transitory storage mediums”; 0144; the instructions may be executed by a “processor”] cause the processor to:
generate the fraud classification rules using a trained machine learning program trained based on a data set indicating types of fraud, of a set of different types of fraud, associated with transactions or accounts; [0126; a “trained model” is used; 0125; “building a business rules predictive model”; 0141; this may be related to “fraud-suspicious classes”; 0198; this may be related to a user’s specific account]
access the account data associated with a particular account; [0201; particular transactions associated with the accountholder are analyzed] and
generate… a fraud classification that identifies a particular type of fraud, included in the set of different types of fraud, associated with the particular account. [0260; a “compromise, takeover, or suspicious activity of an accountholder’s account” may be identified]
Adjaoute does not explicitly teach determining a type of fraud by applying the fraud classification rules to the account data, but it is known in the art. Ben-Or teaches a risk management system [title] that uses machine learning. [Col. 3, line 43] It may apply rules to transactions associated with accounts to determine fraud. [Col. 9, lines 35-36, 46-49] Ben-Or and Adjaoute are analogous art as each is directed to electronic means for using machine learning to identify information associated with fraud in financial transactions.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Ben-Or with that of Adjaoute in order to better identify complex fraud schemes, as taught by Ben-Or; [Col. 1, line 14] further, it is simply a substitution of one known part for another with predictable results, simply using rules with account data as in Ben-Or rather than the technique of Adjaoute; the substitution produces no new and unexpected result.
With regard to Claim 18:
The one or more non-transitory computer-readable media of claim 17, wherein:
the account data comprises transaction data associated with a transaction, and
the fraud classification, generated by applying the fraud classification rules, indicates the particular type of fraud associated with the transaction. [0201; 0260 as cited above in regard to claim 17]
This claim is not patentably distinct from claim 17. First, it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of data which imparts neither structure nor functionality to the claimed medium or the computer executing its instructions. Second, as the data only “comprises” this item, it can include other information, and any further processing can be based entirely on the other information. The reference is provided for the purpose of compact prosecution.
With regard to Claim 19:
The one or more non-transitory computer-readable media of claim 17, wherein:
the computer-executable instructions further cause the processor to determine, based on the account data, that a transaction associated with the particular account is likely to be fraudulent, [0231; “a warning is generated to indicate that the data record is likely fraudulent”]
the fraud classification rules are applied to generate the fraud classification in response to determining that the transaction is likely to be fraudulent, and
the fraud classification identifies the particular type of fraud associated with the transaction. [Ben-Or in combination with Adjaoute as cited above in regard to claim 17; this is simply a substitution of one known part for another with predictable results, simply performing steps for the reason cited herein rather than, or in addition to, the reasons cited in regard to claim 17]
With regard to Claim 20:
The one or more non-transitory computer-readable media of claim 17, wherein:
the computer-executable instructions further cause the processor to:
determine that the particular account is likely to be associated with a broad class of fraud; and
identify, by applying the fraud classification rules, a narrower class of fraud, within the broad class of fraud, that is associated with the particular account, and
the particular type of fraud, indicated by the fraud classification, comprises the narrower class of fraud. [Table 5; “fraud” as opposed to “no fraud” reads on broad classification; 0260 as cited above in regard to claim 17; specific categories of fraud are identified]
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Adjaoute in view of Ben-Or et al. further in view of Duan (U.S. Publication No. 2015/0095247).
With regard to Claim 6:
The computer-implemented method of claim 1, wherein the fraud classification indicates:
a first type of fraud included in the set of different types of fraud,
a second type of fraud included in the set of different types of fraud,
a first probability that the first type of fraud is the particular type of fraud associated with the particular account, and
a second probability that the second type of fraud is the particular type of fraud associated with the particular account.
Adjaoute and Ben-Or teach the method of claim 1, including that fraud is associated with a particular account as cited above, but do not teach these indications, and although they are of no patentable significance as explained below, they are known in the art. Duan teaches a fraud classification system [title] that uses machine-learning algorithms. [0043] Fraud classifications are generated by “aggregating [] fraud probability scores” and “assessing the probability that [an] event profile is fraudulent” based on the scores. [id.] A plurality of classifications are so managed. [0046] Duan and Adjaoute are analogous art as each is directed to the use of machine learning in fraud determinations.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Duan with that of Adjaoute and Ben-Or in order to limit financial loss, as taught by Duan; [0003] further, it is simply a substitution of one known part for another with predictable results, simply interpreting data in the manner of Duan rather than, or in addition to, that of Adjaoute; the substitution produces no new and unexpected result.
This claim is not patentably distinct from claim 1, as it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of data but which imparts neither structure nor functionality to the claimed method. The reference is provided for the purpose of compact prosecution.
Conclusion
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/SCOTT C ANDERSON/Primary Examiner, Art Unit 3694
1 Recentive Analytics, Inc. v. Fox Corp. et al., 134 F.4th 1205, 1216 (Fed. Cir. 2025)
