DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 18, 2025 has been entered.
Claims 13 and 19-20 have been amended.
Claims 1-12 and 16-17 have been cancelled.
Claims 13-15 and 18-20 are pending.
The effective filing date of the claimed invention is July 30, 2021.
Response to Amendment
Amendments to Claims 13 and 19-20 are acknowledged.
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 13-15 and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1
As indicated in the preamble of the claims, the examiner finds the claim is directed to a process, machine, manufacture, or composition of matter.(Claims 13-15 and 18 are processes and Claims 19-20 are machines). Accordingly, step 1 is satisfied.
Step 2A – Prong 1
Exemplary Claim 19 (and similarly Claim 13) recites the following abstract concepts that are found to include abstract idea. Any additional elements will be analyzed under Step 2A-Prong 2 and Step 2B:
a cloud server comprising at least one processor and a non-transitory computer-readable storage medium (additional element(s));
the non-transitory computer-readable storage medium comprises executable instructions (additional element(s));
the executable instructions when provided to and executed by the at least one processor from the non-transitory computer-readable storage medium cause the at least one processor (additional element(s)) to perform operations comprising:
receiving a current transaction sequence compr4ised of item events representing item states for a current transaction (see MPEP 2106.04(a)(2)(III) mental processes, where the act of receiving data of a processed transaction can be performed in the human mind - The courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, “methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’” 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) (“‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’” (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same); See MPEP 2106.04(a)(2)(II)(A) Other examples of "fundamental economic principles or practices" of receiving several transaction data points);
assigning an item event type to each item event, for each item to an automatic classification or a manual classification (see MPEP 2106.04(a)(2)(II)(C) managing personal behavior or interactions between people - Other examples of following rules or instructions recited in a claim include: i. assigning hair designs to balance head shape, In re Brown, 645 Fed. Appx. 1014, 1015-16 (Fed. Cir. 2016) (non-precedential); see also MPEP 2106.04(a)(2)(III)(C) FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). – Mental Processes);
determining elapsed times between each item event for each current item associated with the current transaction (See MPEP 2106.04(a)(2)(III)(C)(2) mental processes - Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were “the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries.” 839 F.3d. at 1094-95, 120 USPQ2d at 1296.);
aggregating particular item events to produce aggregate events (See MPEP 2106.04(a)(2)(III)(C)(A) mental processes - a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016););
for each item event sequence associated with the current transaction sequence, providing, to a trained machine-learning model as input, corresponding item events along with a corresponding item event type, corresponding elapsed times, and corresponding aggregate events (see MPEP 2106.05(f) - TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. And See MPEP 2106.04(a)(2)(I) where the use of the model is a mathematical concept; and providing data to a model can be fundamental economic practices or principles of MPEP 2106.04(a)(2)(II)(A) - The court described the claims as an “attempt to patent the use of the abstract idea of [managing a stable value protected life insurance policy] and then instruct the use of well-known [calculations] to help establish some of the inputs into the equation.” 687 F.3d at 1278, 103 USPQ2d at 1433 (alterations in original) (citing Bilski).);
for each item event sequence associated with each item of the current transaction, receiving as output from the trained machine-learning model (see July 2024 Subject Matter Eligibility Examples, Example 47, claim 2 – mathematical concept), an item non-fraud score (See MPEP 2106.04(a)(2)(III)(C) – mental processes - FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016); see also MPEP 2106.04(a)(2)(I)(C) – mathematical calculations - Examples of mathematical calculations recited in a claim include: ii. calculating a number representing an alarm limit value using the mathematical formula ‘‘B1=B0 (1.0–F) + PVL(F)’’, Parker v. Flook, 437 U.S. 584, 585, 198 USPQ 193, 195 (1978)); and
providing a corresponding item non-fraud score for each item of the transaction to a fraud detection system configured to determine a likelihood that the current transaction is associated with sweethearting fraud, wherein providing further includes calculating an aggregate transaction non-fraud score from corresponding item non-fraud scores and providing the aggregate transaction non-fraud score to the fraud detection system (See MPEP 2106.04(a)(2)(III)(C) FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). – Mental Process, and See MPEP 2106.04(a)(2)(I)(C) Examples of mathematical calculations recited in a claim include: ii. calculating a number representing an alarm limit value using the mathematical formula ‘‘B1=B0 (1.0–F) + PVL(F)’’, Parker v. Flook, 437 U.S. 584, 585, 198 USPQ 193, 195 (1978) – Mathematical Calculations); and
raising an alert with a particular item non-fraud score to the fraud detection system when the particular item non-fraud score falls below a configured threshold (See MPEP 2106.04(a)(2)(II)(C) managing personal behavior or relationships or interactions between people - An example of a claim reciting managing personal behavior is Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 115 USPQ2d 1636 (Fed. Cir. 2015). The patentee in this case claimed methods comprising storing user-selected pre-set limits on spending in a database, and when one of the limits is reached, communicating a notification to the user via a device. 792 F.3d. at 1367, 115 USPQ2d at 1639-40. The Federal Circuit determined that the claims were directed to the abstract idea of “tracking financial transactions to determine whether they exceed a pre-set spending limit (i.e., budgeting)”, which “is not meaningfully different from the ideas found to be abstract in other cases before the Supreme Court and our court involving methods of organizing human activity.” 792 F.3d. at 1367-68, 115 USPQ2d at 1640.);
wherein the fraud detection system is trained on historical sequences to optimize a number of states in a state graph (See MPEP 2106.04(a)(2)(I) where the use of the model is a mathematical concept; and providing data to a model can be fundamental economic practices or principles of MPEP 2106.04(a)(2)(II)(A) - The court described the claims as an “attempt to patent the use of the abstract idea of [managing a stable value protected life insurance policy] and then instruct the use of well-known [calculations] to help establish some of the inputs into the equation.” 687 F.3d at 1278, 103 USPQ2d at 1433 (alterations in original) (citing Bilski)., and see July 2024 Subject Matter Eligibility Examples, Example 47, claim 2 – mathematical concept applied to mental processes);
wherein the corresponding item non-fraud scores and the aggregate transaction non-fraud score are integrated into existing fraud detection capabilities of the fraud detection system (See MPEP 2106.04(a)(2)(I) where the use of the model is a mathematical concept; and providing data to a model can be fundamental economic practices or principles of MPEP 2106.04(a)(2)(II)(A) - The court described the claims as an “attempt to patent the use of the abstract idea of [managing a stable value protected life insurance policy] and then instruct the use of well-known [calculations] to help establish some of the inputs into the equation.” 687 F.3d at 1278, 103 USPQ2d at 1433 (alterations in original) (citing Bilski)., and see July 2024 Subject Matter Eligibility Examples, Example 47, claim 2 – mathematical concept applied to mental processes).
When viewed alone and in ordered combination, the examiner finds that these claim limitations recite abstract idea. Thus, Claim 19 (and similarly Claim 13) recites abstract idea.
Step 2A – Prong 2
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
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)
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
The identified abstract idea of exemplary Claim 19 (and similarly Claim 13) is not integrated into a practical application. The additional elements are: a cloud server comprising at least one processor and a non-transitory computer-readable storage medium, and a transaction terminal. These additional elements are broadly recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). In other words, limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include: i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. Accordingly, Claim 19 (and similarly Claim 13) is directed to abstract idea.
Step 2B
Claim 19 (and similarly Claim 13) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, a cloud server comprising at least one processor and a non-transitory computer-readable storage medium, and a transaction terminal, do not add significantly more to the exception because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). In other words, limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include: i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). Claim 19 (and similarly Claim 13) is ineligible as directed to abstract idea.
Claim 14 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III)(C).
Claim 15 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III)(C).
Claim 18 (and similarly Claim 20) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III)(C). For the additional limitation of a Software-as-a-Service (SaaS) accessible to a retailer server, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Prior Art
The prior arts of record fail to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Exemplary claim 19 recites the following:
a cloud server comprising at least one processor and a non-transitory computer-readable storage medium;
the non-transitory computer-readable storage medium comprises executable instructions;
the executable instructions when provided to and executed by the at least one processor from the non-transitory computer-readable storage medium cause the at least one processor to perform operations comprising:
receiving a current transaction sequence comprised of item events representing item states for a current transaction;
assigning an item event type to each item event, for each item to an automatic classification or a manual classification;
determining elapsed times between each item event for each current item associated with the current transaction;
aggregating particular item events to produce aggregate events;
for each item event sequence associated with the current transaction sequence, providing, to a trained machine-learning model as input, corresponding item events along with a corresponding item event type, corresponding elapsed times, and corresponding aggregate events;
for each item event sequence associated with each item of the current transaction receiving as output from the trained machine-learning model, an item non-fraud score; and
providing a corresponding item non-fraud scores for each item of the transaction to a fraud detection system configured to determine a likelihood that the current transaction is associated with sweethearting fraud, wherein providing further includes calculating an aggregate transaction non-fraud score form corresponding item non-fraud scores and providing the aggregate transaction non-fraud score to the fraud detection system; and
raising an alert with a particular item non-fraud score to the fraud detection system when the particular item non-fraud score falls below a configured threshold;
wherein the fraud detection system is trained on historical sequences to optimize a number of states in a state graph;
wherein the corresponding item non-fraud scores and the aggregate transaction non-fraud score are integrated into existing fraud detection capabilities of the fraud detection system. (Emphasis added to highlight features that distinguish over the prior art).
As further explained below, the prior art of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the Applicant’s claimed invention.
US Pat Pub. 2017/0140384 “Zoldi” discloses using archetype-based n-grams based on an event sequence of the real-time transactions, the n-grams providing a probability based on a specific sequence of behavioral events and their likelihood, and in which high probability n-grams represent typical behaviors of customers in a same peer group, and low probability n-grams represent rare event sequences and increased risk. Zoldi fails to disclose analyzing item state transitions specifically for sweethearting fraud detection in retail environments that permits evaluation of the current transaction sequence occurring before and subsequent to any sweethearting fraud, which quantifies and yields objective information.
US Pat Pub 2012/0321146 “Kundu” teaches alerting store personnel when various activities, events, conditions, etc., occur at the checkout in retail establishments. For example, in accordance with one embodiment, the alerting can take place in substantially real-time, when the event occurs, allowing personnel to take appropriate measures, corrective or otherwise, to deal with the detected event. Examples of such events may include situations such as when a person of interest is detected as shopping at the store, when a cashier has missed scanning an item at the checkout, or to alert store personnel if a non-empty shopping cart has exited the store without payment (a.k.a., a cart push-out). Kundu fails to teach analyzing item state transitions specifically for sweethearting fraud detection in retail environments that permits evaluation of the current transaction sequence occurring before and subsequent to any sweethearting fraud, which quantifies and yields objective information.
US Pat Pub 2019/0385170 “Arrabothu” teaches receiving a transaction authorization request comprising transaction details; inputting the transaction details into a fraud scoring system comprising a fixed fraud detection model; inputting the transaction details into a neural network comprising an improvable fraud detection model; applying the fixed fraud detection model and the improvable fraud detection model to the transaction details; producing a fraud score in response to applying the fixed fraud detection model to the transaction details and a neural network fraud score in response to applying the improvable fraud detection model to the transaction details. Arrabothu fails to teach analyzing item state transitions specifically for sweethearting fraud detection in retail environments that permits evaluation of the current transaction sequence occurring before and subsequent to any sweethearting fraud, which quantifies and yields objective information.
US Pat Pub 2022/0138864 “Edwards” teaches a rule-based reasoning system may receive first transaction-level data for a first transaction that indicates a first transaction amount of the first transaction and a first merchant associated with the first transaction. The system may determine first item-level data for the first transaction that indicates one or more line items associated with the first transaction. The system may infer second item-level data for a second transaction based on the first item-level data. Edwards fails to teach analyzing item state transitions specifically for sweethearting fraud detection in retail environments that permits evaluation of the current transaction sequence occurring before and subsequent to any sweethearting fraud, which quantifies and yields objective information.
US Pat Pub 2021/0365922 “Bloom” teaches receiving a transaction request from a service. The transaction request may include contextual data related to the transaction request. The system may be further adapted to determine that the contextual data meets the respective transactional parameters of a personalized transaction rule of the set of personalized transaction rules. Bloom fails to teach analyzing item state transitions specifically for sweethearting fraud detection in retail environments that permits evaluation of the current transaction sequence occurring before and subsequent to any sweethearting fraud, which quantifies and yields objective information.
Response to Arguments
35 USC 101
Applicant's arguments filed December 18, 2025 have been fully considered but they are not persuasive.
Applicant argues that the amended claims should be eligible under the same rationale as the Ex parte Desjardins, Appeal 2024-000567 (Sept. 26, 2025) decision.
First Applicant argues that limitations constitute improvements to how the fraud detection system itself operates as precisely the type of improvement found eligible in Desjardins. However, the analysis provided for why Desjardins is found eligible did not include improvements to the system that machine learning was being applied, but instead to improvements to the actual machine learning. The Desjardins decision recites:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ¶ 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.
As such, the claims are found to be ineligible.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REVA R MOORE whose telephone number is (571)270-7942. The examiner can normally be reached M-Th: 9:00-6:00.
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/REVA R MOORE/Examiner, Art Unit 3627
/PETER LUDWIG/Primary Examiner, Art Unit 3627
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