DETAILED ACTION
This Non-Final Office action is in response to Applicant’s filing on 03/03/2026. Claims 1-20 are pending. The effective filing date of the claimed invention is 03/06/2018.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 (and similarly claims 8 and 15) recites the limitation “its” in line 15. This renders the claim indefinite as it is unclear to the examiner what the “its” is referring to. The claims require proper antecedent basis, referring to the specific limitations using “the” for instance. For example, if the “its” is referring to the computing terminal, then the claim should recite “the computer terminal”—not “its.” The scope of the claim is unascertainable. Appropriate correction is required.
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 claims are found to be directed to abstract idea.
Step 1 – Claims 1-7 relate to apparatus claims; claims 8-14 relate to process claims; and, claims 15-20 are manufacture claims. Accordingly, step 1 is satisfied.
Step 2A Prong 1- Exemplary claim 1 (and similarly claims 8 and 15) recites the following abstract idea:
A provider computing system comprising: receive a plurality of fraud cases, each fraud case associated with transaction data and having an initial priority score (see MPEP 2106.04(a)(2)(III)(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));
determine an updated priority score for each fraud case based on the transaction data and case prioritization data, the case prioritization data comprising a set of rules developed using a machine learning model (see MPEP 2106.04(a)(2)(III)(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 the machine learning aspects, see Recentive v. Fox, 2023-2437 (Fed. Cir. April 18, 2025), and MPEP 2106.04(a)(2)(I) using machine learning model);
assign each fraud case to one of a plurality of queues, each fraud case assembled in a fraud case database (see MPEP 2106.04(a)(2)(II)(C) 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); further see MPEP 2106.04(a)(2)(II)(B) An example of a claim reciting business relations is found in Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 123 USPQ2d 1100 (Fed. Cir. 2017). The business relation at issue in Credit Acceptance is the relationship between a customer and dealer when processing a credit application to purchase a vehicle. The patentee claimed a “system for maintaining a database of information about the items in a dealer’s inventory, obtaining financial information about a customer from a user, combining these two sources of information to create a financing package for each of the inventoried items, and presenting the financing packages to the user.”);
assign at least one fraud case to a fraud agent computing terminal associated with a fraud agent responsive to determining that its updated priority score is at or above a threshold, wherein assigning the at least one fraud case to the fraud agent computing terminal comprises moving the at least one fraud case to a cache (see e.g. MPEP 2106.04(a)(2)(II)(A-B));
receive an input from the fraud agent computing terminal regarding a disposition of the at least one fraud case (see e.g. MPEP 2106.04(a)(2)(III)); and
restructure the case prioritization data based on the input (see e.g. MPEP 2106.04(a)(2)(II)(C)).
When viewed alone and in ordered combination, these limitations are found to recite abstract idea.
Step 2A Prong 2 – Exemplary claim 1 is not found to integrate the abstract idea into practical application. Exemplary claim 1 recites the additional elements of: a network interface structured to facilitate data communication via a network; a database structured to store information associated with accounts held by an institution associated with the provider computing system; and a processing circuit comprising a processor and a memory, the processing circuit structured to [implement the abstract idea]. See MPEP 2106.05(f) “apply it” rationale, where As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
As for the machine learning aspect, this is also performed in “apply it” manner – see Recentive, as attached.
Accordingly, when viewed alone and in ordered combination, exemplary claim 1 is found to be directed to abstract idea.
Step 2B – The examiner does not find claim 1 (8 or 15) to include significantly more. The additional element analysis from Step 2A Prong 2 is equally applied to Step 2B. Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis.
The courts have recognized the following computer functions as well‐understood, routine, and conventional (“WURC”) functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Claim 1 includes limitations relating to receiving cases/data over the claimed network, where this has been found to be WURC: i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added));
For the limitations relating to moving data, restructuring data, assigning data to database(s), etc., this has been found to be WURC: iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining “shadow accounts”); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Accordingly, when these limitations are viewed alone and in ordered combination, claim 1 is found to be directed to abstract idea.
Dependent claims – Claims 2, 9, 16 recite more abstract idea performed in apply it manner. See MPEP 2106.04(a)(2)(III). Claims 3, 10, 17 recite more abstract idea. See MPEP 2106.04(a)(2)(II)(A-B). Claims 4, 11, and 18 recite more abstract idea (MPEP 2106.04(a)(2)(III)) performed with WURC activity. Claims 5, 12, and 19 recite more abstract idea of storing data, and via WURC and apply it. Claims 6, 13 recite more abstract idea. See MPEP 2106.04(a)(2)(I) and Recentive. Claims 7, 14, 20 are directed to abstract idea. See Recentive.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. No. 9,954,879 to Sift (“Sift”) in view of U.S. Pat. Pub. No. 2018/0182029 to Vinay (“Vinay”).
With regard to claims 1, 8, and 15, Sift discloses the claimed provider computing system comprising:
a network interface structured to facilitate data communication via a network (see e.g. Sift col. 9, line 17-25);
a database structured to store information associated with accounts held by an institution associated with the provider computing system (see e.g. Sift col. 9 ln 45 – col. 10 ln 16, discussing the storage of event data associated with the digital events; Sift does not explicitly disclose the claimed “database” that stores data with accounts held by institution; See Vinay [0002-4] [0015-19] [0073-78], teaches a financial institution maintaining modeling systems with access to credit-worthiness data, historic transaction data, historic fraudulent data, current transaction data, and transaction accounts used at merchant POS systems. Therefore, it would have been obvious to one of ordinary skill in the fraud assessment art before the effective filing date of the claimed invention to modify Sift’s machine learning fraud mitigation system to operate in Vinay’s financial institution transaction account environment, as “[0003] Once credit is extended to card members, their accounts may occasionally be compromised. Financial institutions offering transaction accounts may protect users against fraud by reimbursing account holders for fraudulent charges. As such, financial institutions seek to minimize fraud in order to minimize their losses as a result of reimbursement expenses. Financial institutions may implement risk assessment systems to assess risk, but those systems have traditionally been only as effective as the expert-written rules guiding the systems.” Vinay [0003]); and
a processing circuit comprising a processor and a memory, the processing circuit (see e.g. Sift col. 7 ln 55-65; Sift col 14, ln 25-40) structured to:
receive a plurality of fraud cases, each fraud case associated with transaction data (see e.g. Sift abstract, Sift col. 4 ln 40-58, col 1 ln 13-17 This invention relates generally to the digital fraud and abuse field, and more specifically to a new and useful system and method for generating and implementing digital threat mitigation applications in the digital fraud and abuse field.) and having an initial priority score (see e.g. Sift col 2, ln 50-67);
determine an updated priority score for each fraud case based on the transaction data and case prioritization data (see e.g. Sift col 6, ln 50-65), the case prioritization data comprising a set of rules developed using a machine learning model (see e.g. Sift col 12, ln. 11-15; Sift col. 12 ln 39-50);
assign each fraud case to one of a plurality of queues, each fraud case assembled in a fraud case database (e.g. Sift col 6 ln 48 – col 7 ln 11, where the queue engine can divide the cases up into queues by assigning to different individuals, for instance, see also where “The reviewing queue engine 140 additionally functions to arrange the received triggering digital events according to a priority (e.g., according to highest probability of fraud, based on time of receipt or occurrence, or according to greatest potential loss due to fraud, and the like).” Where these could be considered to be a plurality of queues, breaking up the events into highest probability of fraud, based on time of receipt, and the like.);
assign at least one fraud case to a fraud agent computing terminal associated with a fraud agent responsive to determining that its updated priority score is at or above a threshold, wherein assigning the at least one fraud case to the fraud agent computing terminal comprises moving the at least one fraud case to a cache (e.g. Sift col 6 ln 30—col 7 ln 11, discloses routing/flagging digital events requiring additional scrutiny to a reviewing queue engine when confidence is below a threshold, and the review queue includes manual review by human analysts/experts);
receive an input from the fraud agent computing terminal regarding a disposition of the at least one fraud case (e.g. Sift abstract, “using digital fraud policy to configure a second computing node comprising a decisioning API or a decisioning computing server to automatically evaluate and automatically select one digital event processing outcome of a plurality of digital event processing outcomes that indicates a disposal of the digital events classified as the digital event type”); and
restructure the case prioritization data based on the input (e.g. Sift col 7 ln 23-55, The disposal decision generated at the reviewing engine queue 140 together with the review input may, in turn, be converted by the system 100 to useable machine learning input into the machine learning digital fraud detection system 120. Thus, the reviewing queue input and disposal decision may be consumed by the machine learning digital fraud detection system 120 as machine learning training data that may be used to adjust weightings of one or more factors of or add new factors (features) with weightings to the existing machine learning models implemented by the machine learning digital fraud detection system 120 thereby improving the technical capabilities of the machine learning digital fraud detection system 120 to evaluate and determine a digital threat level (e.g., digital threat score) associated with digital event data.).
With regard to claims 2, 9, and 16, Sift further discloses the plurality of fraud cases are received from a fraud identification system, and wherein the fraud identification system assigns the initial priority score (see e.g. col. 2 ln 50—col. 3 ln 6, Sift’s score API/machine learning system generates a digital threat score indicating likelihood of fraud or abuse.).
With regard to claims 3, 10, and 17, Sift further discloses where the updated priority score is higher than the initial priority score (Sift, the review queue shown above and disposal decisions may be consumerd as training data to adjust weights or add new features to existing machine learning models, improving the systems ability to determine a digital threat level such as a digtal threat score. This supports the idea that a later or updated score can differ from an earlier score. Sift does not explicitly state that the updated score is higher than the original score. Sift discloses updating/retraining/and rescoring. When combined with Vinay’s financial institutional aspects, thus in a financial fraud workflow, when later-applied prioritization data or model output identifies a case as more suspicious than initially scored, the updated fraud priority risk score would be higher than the initial score).
With regard to claims 4, 11, and 18, Sift further discloses assigning the at least one fraud case to the fraud agent computing terminal associated with the fraud agent comprises: receiving, from the fraud agent computing terminal, a request to return a highest priority fraud case; determining the highest priority fraud case by identifying the at least one fraud case as having a highest updated priority score; and transmitting the at least one fraud case to the fraud agent computing terminal (see e.g. Sift col 6 ln 48—col 7 ln 11).
With regard to claims 5, 12, and 19, Sift further discloses the processing circuit is further structured to store the plurality of fraud cases and the updated priority score for each fraud case in a central case database (Sift above discussing the queuing ability of such data; Vinay also teaches a financial risk system trained on datasets and applying risk models to financial-service requests to generate risk assessments; see combination above).
With regard to claims 6 and 13, Sift further discloses the machine learning model comprises at least one of a supervised learning model, an unsupervised learning model, or a reinforcement learning model (Sift col 4, ln 40—col 5 ln 10).
With regard to claims 7, 14, and 20, Sift further discloses the machine learning model is retrained based on the input, and wherein the case prioritization data is updated based on an output received from the retrained machine learning model (Sift col 7 ln 10-60).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Peter Ludwig whose telephone number is (571)270-5599. The examiner can normally be reached Mon-Fri 9-5.
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/PETER LUDWIG/Primary Examiner, Art Unit 3627
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