DETAILED ACTION
Acknowledgements
This action is in response to Applicant’s filing on Nov. 21, 2025, and is made Non-Final. This action is being examined by James H. Miller, who is in the eastern time zone (EST), and who can be reached by email at James.Miller1@uspto.gov or by telephone at (469) 295-9082.
Interviews
Examiner interviews are available by telephone or, preferably, by video conferencing using the USPTO’s web-based collaboration platform. Applicants are strongly encouraged to schedule via the USPTO Automated Interview Request (AIR) portal at http://www.uspto.gov/interviewpractice. Interviews conducted solely for the purpose of “sounding out” the examiner, including by local counsel acting only as a conduit for another practitioner, are not permitted under MPEP § 713.03. The Office is strictly enforcing established interview practice, and applicants should ensure that every interview request is directed toward advancing prosecution on the merits in compliance with MPEP §§ 713 and 713.03.
For after-final Interview requests, supervisory approval is required before an interview may be granted. Each AIR should specifically explain how the After-Final Interview request will advance prosecution—for example, by identifying targeted arguments responsive to the rejection of record, alleged defects in the examiner’s analysis, proposed claim amendments, or another concrete basis for discussion. See MPEP § 713. If the AIR form’s character limits prevent inclusion of all pertinent details, Applicants may send a contemporaneous email to the examiner at James.Miller1@uspto.gov.
The examiner is generally available Monday through Friday, 10:00 a.m. to 4:00 p.m. EST.
For any GRANTED Interview Request, Applicant can expect an email within 24 hours confirming an interview slot from the dates/times proposed and providing collaboration tool access instructions. For any DENIED Interview Request, the record will include a communication explaining the reason for the denial.
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 Nov. 21, 2025, has been entered.
Claim Status
The status of claims is as follows:
Claims 25–44 remain pending and examined with Claims 25, 35, and 41 in independent form.
Claims 25, 34, 35 and 41 are presently amended.
No Claims are presently cancelled or added.
Response to Amendment
Applicant's Amendment has been reviewed against Applicant’s Specification filed Jul. 1, 2022, [“Applicant’s Specification”] and accepted for examination.
Response to Arguments
35 U.S.C. § 101 Argument
1) Applicant argues traditional AML systems generate a high volume of alerts, only a small fraction of which lead to SARs, so there is a technical need to prioritize alerts and focus on cases with the highest likelihood of laundering (¶ 5). The claimed system uses threat scores and account linking to prioritize rules triggered cases and reduce false positives (¶ 30). These features are an improvement in technology.
Examiner respectfully disagrees.
The problem described in ¶ 5 and ¶ 30 is business oriented—how to allocate human investigation resources and reduce false positives in compliance workflows. Claim 25 implements this by computing scores and then prioritizing accounts, which is a form of information analysis and decision making that can be done conceptually by a human using pen and paper for a small number of entities. The mere fact that a computer is used to automate this prioritization does not make it a technical improvement to the computer itself.
2) Applicant argues profiles efficiently summarize past behavior in a data store while minimizing storage and lookup requirements (¶ 28). Behavior sorted lists (BLists) are space limited data structures that track information in a space efficient manner using a weighting mechanism to preserve frequently seen favorites (¶¶ 38–9). These features are an improvement in technology.
Examiner respectfully disagrees.
¶28 and ¶¶ 38–9 do describe a bounded data structure that aims to reduce storage and lookup costs compared to storing full histories. However, Claim 25 does not positively recite the BList capacity or any specific storage bounds, and the general idea of maintaining a list of recent/frequent items using weights and recency is a well-known computer science pattern that can be conceptualized and carried out manually for small lists. At best, this shows an implementation choice for data summarization, not a concrete improvement to the operation of the computer as such.
3) Applicant argues profiles do not store a set of previous records but use recursive functions such as exponential decays to compute summary statistics of behavior, which is said to be efficient. For risk linking, various decay strategies (event averaged, time based, time to live) are used to update risk linked scores and improve efficiency versus graph-based analytics. These features are an improvement in technology.
Examiner respectfully disagrees.
Recursion, exponential decay, and decay strategies based on events or time are standard algorithmic techniques known in the art and taught in introductory courses. Claim 25 only broadly recites “decaying the first weight” without specifying any particular algorithm, parameterization, or hardware effect. Even in a two-profile embodiment, a human could maintain two short lists and manually apply simple decay rules to weights using pen and paper. The claimed use of decay functions, therefore, does not meaningfully limit the abstract idea of tracking and updating behavioral summaries.
4) Applicant argues the AML Threat Score system reduces false positives and provides more stable scores over time, thus improving functional robustness (¶ 30, ¶¶ 66–7). Profiles and features are designed to minimize storage and lookup requirements, contributing to efficient and accurate scoring (¶ 28). These features are an improvement in technology.
Examiner respectfully disagrees.
Reduced false positives and improved score stability are business performance metrics, not in themselves technical improvements to computer architecture or core operation. The specification describes using more informative features and training data to better distinguish true from false alarms (¶¶ 66–7), which is a classic data science goal rather than a change in how the computer functions at a low level. Claim 25 simply implements this business objective using generic data structures and computations, which remains within the realm of abstract data analysis and decision making.
5) Applicant argues BLists use a weighting mechanism tuned so that frequently seen favorites are preserved, gated on recency, while newly seen items can be added without deleting long term often seen items (¶ 38). The example in ¶ 40 shows how repeated observations update weights and move entries to the “top favorite” position. These features are an improvement in technology.
Examiner respectfully disagrees.
¶¶ 38–40 describe a particular policy for managing entries in a list using weight and recency, which is more specific than simply saying “keep important items.” However, Claim 25 only generically recites that the weight is “updatable” and used to decide whether entries are deleted or preserved based on a recency value; it does not require any specific threshold, function, or list capacity that would tie it to a particular computer improving algorithm. A human could implement an analogous policy for two short lists by hand (e.g., “keep top N behaviors, older ones fall off”), so this still reflects mental type evaluation rather than a non-abstract technical solution.
6) Applicant argues BList payloads include risk linking features that allow high scoring risky entities to influence scores of other associated entities in near real time, without computing costly graph-based analytics. The risk linking mechanism provides a fast and efficient way of linking accounts via stored scores and BLists rather than explicit graph traversal. These features are an improvement in technology.
Examiner respectfully disagrees.
The specification contrasts the disclosed approach with “costly graph based analytics,” but Claim 25 does not recite any graph traversal or avoidance of graph operations, nor does it specify particular performance constraints or data store structures implementing the operations. In the claims, the “linking” step is recited in functional terms—updating a payload and generating scores influenced by other scores—which is still just information propagation between entities based on their similarity. This can be conceptualized and performed manually for two entities and two lists and remains within the abstraction of mental reasoning about relationships and risk.
7) Applicant analogizes BLists and profiles to the self-referential table in Enfish, arguing that they improve data storage and retrieval by efficiently summarizing past behavior and supporting fast similarity matching based on permutations of keys. The specification notes that permutation based fingerprinting can be fast enough for real time decisioning and trades space for time (¶¶ 45, 46). These features are an improvement in technology.
Examiner respectfully disagrees.
Enfish involved claims that on their face defined a new logical table structure with specific characteristics that improved the way the database itself stored and retrieved data. Here, Claim 25 only refers to “behavior sorted lists” and does not claim the BList tuple structure or permutation indexing explicitly. The efficiency and behavior attributed to permutations in ¶¶ 45, 46 are description level details and not recited by the claims.
8) Applicant argues the claimed process cannot be practically performed in the human mind because it involves recursive updates, permutation-based fingerprinting, and maintenance of profiles and BLists that can cover all customers and accounts worldwide. These features are an improvement in technology.
Examiner respectfully disagrees.
Eligibility is evaluated based on the claims and Claim 25 is fully satisfied by a minimal embodiment involving only two profiles and two behavior sorted lists for two entities; at that scale, a human can track behaviors, update weights, compare lists, compute a simple numerical similarity, decide to link accounts, adjust scores, and prioritize an account using pen and paper. The large-scale deployment scenario in ¶ 65 is an implementation choice, not a requirement of the claim. Thus, the core steps remain mental process type information analysis and decision making, merely automated on a generic computer.
9) Applicant agues the claims are not directed to managing relationships, social activities, or advertising, but to computer-based machine learning systems for detecting and preventing money laundering. Therefore, they should not fall within the “certain methods of organizing human activity” category.
Examiner respectfully disagrees.
The domain is explicitly financial compliance and AML, which involves commercial interactions and regulatory reporting (¶¶ 2–5, 27–30), a recognized subset of “certain methods of organizing human activity.” Claim 25’s focus on scoring entities and prioritizing accounts for investigation is part of managing financial risk and investigative workflow. While implemented on a computer, the claim still centers on organizing and prioritizing business activities rather than improving the underlying technology.
10) Applicant argues like McRO, the claim recites specific rules (weight/recency policies, decay strategies, permutation based matching) that improve how the computer performs behavioral profiling and account linking.
Examiner respectfully disagrees.
The specification clearly details rules for how weights are updated and how lists may be compared or fingerprinted, but Claim 25 itself recites these at a relatively high, functional level—“decaying the first weight,” “comparing entries,” “generating a numerical value,” and “linking” accounts based on “consistency” measures—without specifying particular rule sets, thresholds, or algorithms. In McRO, the claims themselves recited very specific rule sets that fundamentally changed how lip synchronization was performed. Here, the claim reads more as a generalized scoring flow that a human analyst could emulate conceptually, not a tightly defined technical rule set.
11) Applicant argues that, although the system uses mathematical constructs such as exponential decays, quantiles, and distance metrics, those are used internally to improve detection performance and efficiency, similar to Ex parte Desjardins.
Examiner respectfully disagrees.
The presence of mathematical formulas and distance metrics alone is not dispositive, but Claim 25 ultimately claims the result of those computations (a “numerical value” representing consistency and a “threat score” used for prioritization) in a way that fits the pattern of abstract data analysis followed by a business oriented decision. The claim does not recite any particular mathematical formulation or constraint that changes how the computer itself operates; it only requires that some distance model or decay be applied, which remains within the realm of generic mathematical manipulation of data. Thus, even under Desjardins, the claim remains directed to a mental process/abstract idea implementation that does not rise to a technical improvement
12) Applicant argues the invention is necessarily rooted in computer technology because it addresses problems of large scale transaction networks, storage limits, and expensive graph analytics by using behavior sorted lists and risk linking features.
Examiner respectfully disagrees.
The underlying problem—detecting and prioritizing money laundering risk—is a business/compliance problem that predates computer networks (¶¶ 2–5), and the specification presents the computer as a tool to scale that analysis (¶¶27–30, 65). Unlike DDR Holdings, which dealt with a specific Internet centric problem, Claim 25 does not solve a problem unique to networked computers; it automates risk scoring and prioritization that a human could do for a small number of entities. The references to storage limits and graph costs in ¶ 29 and ¶ 65 are descriptive context, not claim limitations, so the claim remains directed to abstract information processing.
13) Applicant argues that, even if individual techniques are known, the ordered combination—recursive profiles, space limited BLists with weight/recency/decay, fingerprinting using BLists, and payload based risk linking and score propagation—creates a non-conventional architecture that provides improved computational efficiency, akin to the inventive concept in BASCOM.
Examiner respectfully disagrees.
The claim language does not capture the full architectural detail of ¶¶ 32, 38–41, 42–46, 69–75, 79, and 80. Instead, it recites generic steps—updating profiles, adding entries with weights, decaying weights, comparing two lists, generating a numerical value, linking accounts, generating scores, and prioritizing accounts—that describe a high level workflow for behavior based risk scoring. Each of these building blocks (list maintenance, decay, similarity scoring, and score based prioritization) is itself a known, routine technique, and the claim does not impose structural or algorithmic constraints that would distinguish the ordered combination from a generic implementation of behavioral risk scoring on a conventional computer. Accordingly, the claim does not recite the kind of non conventional, non generic arrangement of elements that provided the inventive concept in BASCOM.
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 25–44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Analysis
Step 1: Claims 25–44 are directed to a statutory category. Claims 25–34 recite a “a computer-implemented system” and are therefore, directed to the statutory category of a “machine.” Claims 35–40 recite a “a computer-implemented method” and are therefore, directed to the statutory category of a “process.” Claims 41–44 recite a “non-transitory computer program product” and are therefore, directed to the statutory category of an "article of manufacture.”
Representative Claim
Claim 25 is representative [“Rep. Claim 25”] of the subject matter under examination and recites, in part, emphasis added by Examiner to identify limitations with normal font indicating the abstract idea exception, bold limitations indicating additional elements. Each limitation is identified by a letter for later use as a shorthand notation in referencing/describing each limitation. Portions of the claim use italics to identify intended use limitations1 and underline, as needed, in further describing the abstract idea exception:
[A] 25. A computer-implemented system comprising at least one processor and a machine-readable medium storing instruction that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
[B] electronically retrieving one or more profiles from a data store system that stores a plurality of profiles associated with a plurality of behavior sorted lists, at least a first profile out of the plurality of profiles being associated with a first entity from among a plurality of entities;
[C] recursively computing summary statistics of behavior of the first entity by adding an observed behavior represented by an input data record for the first entity to a behavior sorted list associated with a first profile wherein the observed behavior is added to the first behavior sorted list in association with a first weight; and
[C1] wherein the first weight is updatable and is utilized to determine whether one or more observed behaviors stored in one or more entries in the first behavior sorted list are to be deleted or preserved, a stored behavior entry being replaceable with a first observed behavior based on a recency value associated with the stored behavior entry;
[D] decaying the first weight based on addition or deletion of one or more behavior entries to or from the first behavior sorted list, responsive to the one or more behavior entries being associated with the first profile, the one or more behavior entries being different than a corresponding behavior entry associated with the first weight;
[E] comparing one or more entries in the first behavior sorted list with one or more entries in a second behavior sorted list associated with a second profile, a second entity being associated with the second profile;
[F] generating a numerical value associated with the first behavior sorted list, the numerical value representing a consistency measure between the first behavior sorted list and the second behavior sorted list;
[G] [Intentionally left blank]
[H] linking a first account associated with the first entity with a second account associated with the second entity by updating at least a payload of the first behavior sorted list or the second behavior sorted list, responsive to a consistency distance measure for the first behavior sorted list and the second behavior sorted list; and
[I] based on the linking and payload values of the first behavior sorted list and the second behavior sorted list, generating first threat score for the first entity as influenced by a second threat score generated for the second entity and storing the first threat score in the first account's profile and the second account's profile, responsive to determining that the first account is linked to the second account;
[J] prioritizing rules-triggered identification of the first account for suspicious activity over other accounts based on the generated first threat score and responsive to the linking of the first account and the second account.
Claims are directed to an abstract idea exception.
Step 2A, Prong One: Rep. Claim 25 recites “prioritizing rules-triggered identification of the first account for suspicious activity over other accounts based on the generated first threat [risk] score and responsive to the linking of the first account and the second account” in Limitation J, which recites a fundamental economic principle/practice under the organizing human activity exception because prioritizing suspicious activity deploying based on a risk score “describe[s] concepts relating to the economy and commerce,” such as “hedging … and mitigating risks.” MPEP § 2106.04(a)(2)(II)(A). Limitations B–I are the required steps to prioritize suspicious activity based on a risk score and therefore, recites the same exception. Id.
Alternatively2, Limitations B–J, as drafted, recite the abstract idea exception of mental processes that under the broadest reasonable interpretation, cover performance in the human mind or with pen and paper, but for the recitation of the generic computer components indicated in bold. MPEP § 2106.04(a)(2)(III).
Claims recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
• 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);
. . .
• a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011).
MPEP § 2106.04(a)(2)(III)(A). For example, but for the generic computer components claim language, here, Limitations B–J, recite collecting information (Limitations B, J) and analyzing it (Limitations C, C1, D, E, F, H, I), where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind. For example, Limitations C and D are mental processes that are practically be performed in the human mind or with pen and paper as demonstrated by prior art Zoldi, U.S. Pat. Pub. No. 2010/0228580, ¶¶ 33–43, Table 1 (computation by hand in a Table) (cited on PTO-892) and because it requires mere “observation, evaluation, judgment, and/or opinion” to “recursively comput[e] summary statistics of behavior of the first entity” in the manner claimed. Limitation C1 is also a mental process that is practically performed in the human mind or with pen and paper because it requires mere “observation, evaluation, judgment, and/or opinion” to determine whether “a stored behavior being replaceable [future action] with a first observed behavior based on a recency value associated with the stored behavior entry” and “updatable”. Limitation C1 covers any solution to determine whether “a stored behavior being replaceable with a first observed behavior based on a recency value associated with the stored behavior entry” and “updatable” with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which is so broad as to encompass mental processes. Limitations E is a mental process that is practically performed in the human mind or with pen and paper because collecting and comparing known information (i.e., one entry of a first behavior sorted list and one entry in a second behavior sorted list) are steps that can be practically performed in the human mind under Classen. Limitation F is a mental process that is practically be performed in the human mind or with pen and paper because it requires mere “observation, evaluation, judgment, and/or opinion” to generate a “numerical score” representing consistency between two behavior sorted lists. Limitation F covers any solution to generate a “numerical score” representing consistency between two behavior sorted lists with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which is so broad as to encompass mental processes. Limitation H is a mental process that is practically performed in the human mind or with pen and paper because it requires mere “observation, evaluation, judgment, and/or opinion” to “link[ ]” one account to another responsive to a “consistency distance measure.” Linking is broadly interpreted as “associating” one account to another and may be performed by mental association or by pen and paper through, for example, a written note. Applicant’s Specification describes in an exemplary manner that one method for “executing a distance model” could be by calculating a “Jaccard distance”. Spec., ¶ 43. Leskovec, Jure, Anand Rajaraman, and Jeffrey David Ullman. "Mining of Massive Datasets." (2014) (“NPL Leskovec”) is prior art and additional evidence of a human’s ability to calculate a Jaccard distance without the aid of a computer. NPL Leskovec, §§ 3.1, 3.5 (cited on PTO-892). Limitation I is a mental process that is practically be performed in the human mind or with pen and paper because it requires merely mere “observation, evaluation, judgment, and/or opinion” to “generate a first threat score” and “store the first threat score” in the manner claimed. Additionally, the scope of the claims in view of the amount of data manipulations that are required is further evidence of reasonable manipulation by hand.
Additionally3, Limitations C, C1, D, E, F, and I recite mathematical calculations, a particular form of mathematical concepts, because under the broadest reasonable interpretation in light of the specification, said limitations recite a mathematical operation (e.g., Limitations C (“adding”), C1 (“updatable,” “deleted,” “preserved,” “replaceable”), D (“decaying”), E (“comparing”)) or an act of calculating using mathematical methods (Limitation F (any method), I (any method)) to determine a variable or number (Limitations F (“generating … a numerical value”), G (“amount of consistency”), I (“generating first threat score”)). MPEP § 2106.04(a)(2)(I)(C)(i) (“performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018)”)
If a claim limitation under BRI, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract idea exception. MPEP § 2106.04(a)(2)(III). Accordingly, the pending claims recite the combination of these abstract idea exceptions.
Step 2A, Prong Two: Rep. Claim 25 does not contain additional elements that integrate the abstract idea exception into a practical application because the additional elements are mere instructions to apply the abstract idea exception. MPEP § 2106.05(f). The additional elements are: a computer-implemented system comprising at least one processor and a machine-readable medium storing instruction; a data store system; and one or more distance models.
Regarding the computer-implemented system comprising at least one processor and a machine-readable medium storing instruction; a data store system; and one or more distance models, Applicant’s Specification does not otherwise describe them or describes them using exemplary language as a general-purpose computer, as a part of a general-purpose computer, or as any known and exemplary (generic) computer component known in the prior art. Thus, Applicant takes the position that such hardware/software is so well known to those of ordinary skill in the art that no explanation is needed under 35 U.S.C. § 112(a). Lindemann Maschinenfabrik GMBH v. Am. Hoist & Derrick Co., 730 F.2d 1452, 1463 (Fed. Cir. 1984) (citing In re Meyers, 410 F.2d 420, 424 (CCPA 1969) (“[T]he specification need not disclose what is well known in the art”). E.g., Spec. ¶ 94 (These various aspects or features can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose”); ¶ 33 (“This process of combining the input data record with values saved in the profile through mathematical transformations is known "feature construction".”); ¶ 62 (“The presented system uses on boarding information, as well as any updates to the KYC information, presented over the lifetime of the customer's relationship with the institution.”); ¶ 95 (“"machine-readable medium" refers to any computer program product, apparatus and/or device … "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor … machine-readable medium can store such machine instructions non-transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium”); ¶ 96 (“feedback provided to the user can be any form of sensory feedback … and input from the user may be received in any form”); ¶ 96 (any known and generic (exemplary) “computer”). The generic processor, here, appears to perform calculations (functions) that are programmed by software. Spec. ¶¶ 94, 95. This is a computer doing what it is designed to do—performing directions it is given to follow.
Regarding the data store system, Applicant’s Specification does not describe it so Examiner again assumes Applicant intended any known and exemplary storage device. Spec., ¶¶ 15, 86, 28 (“data store system” mentioned by name only). Limitation B describes the “data store system that stores a plurality of profiles associated with a plurality of behavior sorted lists, at least a first profile out of the plurality of profiles being associated with an input data record for a first entity from among a plurality of entities” and communicates with the exemplary computer to retrieve “one or more profiles” (transmitting and receiving data). Limitation C describes that some of the data in the data store system might be “deleted” or “replaced” which is merely receiving and storing data from the exemplary computer. Amended Limitation I recites, “storing the first threat score in the first account’s profile and the second account’s profile.” Examiner assumes the “storing” occurs in the data store system. At this high-level of claiming, Limitations B, C, and I merely invoke computers or other machinery in their ordinary capacity to receive, store, or transmit data or simply add a general-purpose computer or other computer component after the fact to an abstract idea. MPEP § 2106.05(f)(2).
Regarding the one or more distance models, like any model, receives inputs (data) and provides an output (data). Thus, as claimed, the models themselves are created outside of the claimed invention and merely receives data and transmits an output, which does not integrate the judicial exception into a practical application because it uses a computer in its ordinary capacity to receive, store, or transmit data or simply adds a general-purpose computer or computer components after the fact to an abstract idea. MPEP § 2106.05(f)(2). Further, Applicant’s Specification teaches the distance models are exemplary and known in the prior art4. Spec., ¶ 42 (“a number of distance measures can be used”), ¶ 43 (“Set similarity distances such as Jaccard distance can be used to compare A and B”), ¶ 44 (“Edit distances such as Levenshtein distance can be used”), ¶ 46 (“the choice of distance metric is informed by the specifics of the application.”). Thus, the “one or more distance models,” at this high-level of claiming, does not integrate the judicial exception into a practical application because it covers any solution to “executing one or more distance models” with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result. MPEP § 2106.05(f)(1).
Alternatively, using the distance models to “generate a numerical value” recites the abstract idea exception of mathematical concepts. A practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Limitation A describes the processor executing instructions stored in a medium to perform the steps of the claimed invention. This takes a known and exemplary (generic) piece of hardware and describes the functions of receiving, storing, and sending data (instructions) between the processor and storage device, which at this high-level of claiming, merely invokes computers or other machinery in its ordinary capacity to receive, store, or transmit data. MPEP § 2106.05(f)(2). Limitations B–J describe the processor, medium, and instructions, performing the steps of the claimed invention, which represents the abstract idea exception itself. Performing the steps of the abstract idea exception itself simply adds a general-purpose computer after the fact to an abstract idea exception, MPEP § 2106.05(f)(2), or generically recites an effect of the judicial exception. MPEP § 2106.05(f)(3).
Therefore, the claim as a whole, looking at the additional elements individually and in combination, are no more than mere instructions to apply the exception using generic computer components and is not a practical application. MPEP § 2106.05(f). The additional elements do not integrate the abstract idea exception into a practical application because they do not impose any meaningful limits on the abstract idea exception. Accordingly, Rep. Claim 25 is directed to an abstract idea.
Rep. Claim 25 is not substantially different than Independent Claims 35 and 41 and includes all the limitations of Rep. Claim 25. Independent Claims 35 and 41 contain no additional elements. Therefore, Independent Claims 35 and 41 are also directed to the same abstract idea.
The claims do not provide an inventive concept.
Step 2B: Rep. Claim 25 fails Step 2B because the claim as whole, looking at the additional elements individually and in combination, are not sufficient to amount to significantly more than the recited judicial exception. As discussed with respect to Step 2A, Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer and/or generic computer components. MPEP § 2106.05(f). The same analysis applies here in Step 2B. Mere instructions to apply an exception using a generic computer and/or generic computer components cannot provide an inventive concept. MPEP § 2106.05(I).
The additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the identified judicial exception.
The pending claims in their combination of additional elements is not inventive. First, the claims are directed to an abstract idea. Second, each additional element represents a currently available generic computer technology, used in the way in which it is commonly used (individually generic). Last, Applicant’s Specification discloses that the combination of additional elements is not inventive. Spec., ¶ 97 (steps/functions may be performed in any order); ¶¶ 15, 28, 33, 42, 43, 44, 46, 62, 86, 94, 95, 96 (known and generic (exemplary) computer equipment as explained and cited supra.)
Thus, Examiner finds the additional elements of Rep. Claim 25 are elements that have been recognized as well-understood, routine, and conventional (“WURC”) activity in the particular field of this invention based on Applicant’s own disclosure5. Spec. ¶¶ 15, 28, 33, 42, 43, 44, 46, 62, 86, 94, 95, 96, 97; MPEP § 2106.05(d). Specifically, Applicant’s Specification discloses the recited additional elements (i.e., computer-implemented system comprising at least one processor and a machine-readable medium storing instruction; a data store system; and one or more distance models) are generic computer components. The Examiner also finds the functions of receiving, storing, transmitting, and processing (e.g., performing mathematical operations on) data, described in Limitations A–J are all normal functions of a generic computer.
There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements in combination adds nothing that is not already present when looking at the elements individually. Their collective functions merely provide conventional computer implementation of the abstract idea at a high level of generality. Thus, Rep. Claim 25 does not provide an inventive concept.
Rep. Claim 25 is not substantially different than Independent Claims 35 and 41 and includes all the limitations of Rep. Claim 25. Independent Claims 35 and 41 contain no additional limitations. Therefore, Independent Claims 35 and 41 also do not recite an inventive concept.
Dependent Claims Not Significantly More
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. § 101. Dependent claims are dependent on Independent Claims and include all the limitations of the Independent Claims. Therefore, all dependent claims recite the same Abstract Idea. Dependent claims do not contain additional elements that integrate the abstract idea exception into a practical application or recite an inventive concept because the additional elements: (1) are mere instructions to apply the abstract idea exception; and/or (2) further limit the abstract idea exception of the Independent Claims. The abstract idea itself cannot provide the inventive concept or practical application. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claims 26, 36, and 42 (part) all recite “wherein” clauses or limitations that further limit the abstract idea of the Independent Claims but contains the additional elements of: [A] a data structure that captures statistics of the first entity's behavior and [B] wherein the first behavior sorted list is space-limited and improves storage efficiency.
Regarding Limitation A, Applicant’s Specification explains the “profile is a data structure” and “persisted into a data store and is retrieved and updated for each input data record.” Spec., ¶ 32. “[T]he profile does not contain a set of previous records, instead, it uses recursive functions such as exponential decays to compute summary statistics of behavior.” Id. Thus, “values within the profile store long-term and short-term averages of transaction amounts, frequencies of transaction events, and related quantities … [by] combining the input data record [e.g., a single transaction amount] with values saved in the profile [representing summary statistics of behavior, such as, a short-term average of the transaction amount] through mathematical transformations.” Id. at ¶ 33. This method distinguishes historical transactions, which are not stored as claimed, with averages of transaction amounts, for example, which are stored when a new input transaction record is received. Thus, Examiner interprets “a data structure that captures statistics of the first entity's behavior” in view of Applicant’s Specification as the following four steps: (A) receiving an input transaction record (e.g., a single transaction amount); (B) receiving summary statistical values stored in the profile (e.g., short-term average of transaction amounts); (C) calculating a new summary statistical value received in (B) using the received input data record in (A) (e.g., an updated short-term average of transaction amounts); and (D) transmitting and storing the updated summary statistical values. In short, a data structure that captures statistics of the first entity's behavior without storing a record of past activity of the first entity is—merely stored data that is retrieved, mathematically transformed, and then, re-stored. Importantly, any improvement is realized by the abstract idea itself (i.e., the known mathematical concept of recursion)—not in the computer. Accordingly, this limitation merely invokes computers or other machinery in its ordinary capacity to receive, store, or transmit data or simply adds a general-purpose computer or computer components after the fact to an abstract idea, to automate the manual process of “recursion,” a well-known manual programming technique introduced to second-year, college-level computer science majors at Cal Poly Pomona. MPEP § 2106.05(f)(2); see also, Tang, Fang, "CS240 Lecture Notes on Recursion," https://www.cpp.edu/~ftang/courses/CS240/lectures/recursion.htm (Year: 2013); Dr. Daisy Fang's Home Page https://www.cpp.edu/~ftang/ (Year: 2017); and Course Syllabus CS240 https://www.cpp.edu/~ftang/courses/CS240/ (Year: 2016) (citation nos. E**, B**, and A** on IDS filed Sept. 26, 2022).
Regarding Limitation B, Examiner further finds that using frequent behavior sorted lists ("BLists") stored in an entity's profile for efficient storage is WURC activity in the fraud detection field, citing Zoldi, U.S. Pat. Pub. No. 2010/0228580 (Pub. Date: Sept. 9, 2010), ¶ 23 (“Frequent-behavior Sorted Lists are an effective profile-driven analytic technique for fraud detection”), ¶ 41 (“This methodology is essential for fraud detection as a determination of fraud needs to be made on sub-second time scales utilizing profile structures and cannot be based on searches in databases of historical calling behavior.”). Again, the improvement is not in the storage device—it is generic as explained supra. The improvement lies in the abstract idea itself because “values within the profile store long-term and short-term averages of transaction amounts, frequencies of transaction events, and related quantities … [by] combining the input data record [e.g., a single transaction amount] with values saved in the profile [representing summary statistics of behavior, such as, a short-term average of the transaction amount] through mathematical transformations.” Spec. ¶ 33. Storing an average of five transactions (i.e., one average transaction), for example, requires less space than storing five transactions themselves. The storage device is not improved. The method of storing is improved, which is part of the abstract idea exception itself.
Therefore, the additional elements are no more than mere instructions to apply the exception using generic computer components and not a practical application. MPEP § 2106.05(f). An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claims 27, 37, and 42 (part) all recites “wherein” clauses or limitations that further limit the abstract idea of the Dependent Claims 26 (for Claim 27) and 36 (for Claim 37) or Independent Claim 41 (for Claim 42). Dependent Claims 26 and 27, as explained above, further limit the abstract idea of Independent Claims 25 and 35. Dependent Claims 27, 37, and 42 contain no additional elements. Alternatively, to the extent “recursive features” could be considered an additional limitation, “reclusive features” do not require a computer and thus, recite a mental process or mathematical concepts exception. Spec., ¶¶ 32, 33; see also, Tang, Fang, "CS240 Lecture Notes on Recursion," https://www.cpp.edu/~ftang/courses/CS240/lectures/recursion.htm (Year: 2013), p.1 (citation no. E** cited on IDS filed Sept. 26, 2022) (recursion features do not require a computer). An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claims 28, 38, and 43 (part) all recite “wherein” clauses or limitations that further limit the abstract idea of the Dependent Claim 27 (for Claim 28), Claim 37 (for Claim 38), and Claim 42 (for Claim 43). Dependent Claims 27, 37, and 42 as explained above, merely limits the abstract idea of Independent Claims. Dependent Claims 28, 38, or 43 contain no additional elements. An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claims 29, 39, and 43 (part) all recite “wherein” clauses or limitations that further limits the abstract idea of the Dependent Claim 27 (for Claim 29), Claim 37 (for Claim 39), and Claim 42 (for Claim 43). Dependent Claims 27, 37, and 42, as explained above, merely limits the abstract idea of Independent Claims. Dependent Claims 29, 39, and 43 contain no additional elements not otherwise analyzed. “Recursion” as recited by the claims and taught by Applicant’s Specification, Spec., ¶¶ 32, 33, is the same as “summarizing the frequently-observed behavior of the first profile.” Regardless of this interpretation, “summarizing the frequently-observed behavior of the first profile” is not meaningful for the same reasoning as explained in the discussion of Dependent Claims 26, 36, and 42, supra, because it merely invokes computers or other machinery in its ordinary capacity to receive, store, or transmit data to automate the process of “recursion” using a generic computer. MPEP § 2106.05(f)(2). An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claims 30, 40 and 44 all recite “wherein” clauses or limitations that further limits the abstract idea of the Dependent Claims 26, 36, and 42, respectively. Dependent Claims 26, 36, and 42, as explained above, merely limits the abstract idea of Independent Claims. Dependent Claims 30, 40, and 44, contain the additional element of: the data structure captures statistics of the first entity's behavior without storing a record of past activity of the first entity. For the same reasoning as explained in the discussion of Dependent Claims 26, 36, and 42, supra, this additional limitation is not meaningful because it merely invokes computers or other machinery in its ordinary capacity to receive, store, or transmit data to automate the manual process of “recursion” using a generic computer. MPEP § 2106.05(f)(2). An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claim 31 recites a “wherein” clause or limitation that further limits the abstract idea of the Independent Claim 25 and contains no the additional elements. Generating an alert based on the determining step recites a mental process but for the recitation of the generic computer components. An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claim 32 recites a “wherein” clause or limitation that further limits the abstract idea of the Independent Claim 25 and contains no additional elements. Alternatively, to the extent “recursive features” could be considered an additional limitation, “reclusive features” do not require a computer and thus, recite a mental process exception. Spec., ¶¶ 32, 33; Tang, Fang, "CS240 Lecture Notes on Recursion," https://www.cpp.edu/~ftang/courses/CS240/lectures/recursion.htm (Year: 2013), p.1 (citation no. E** cited on IDS filed Sept. 26, 2022) (recursion features do not require a computer). An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claim 33 recites a “wherein” clause or limitation that further limits the abstract idea of the Independent Claim 25 and contains no the additional elements. Payload and the described payload types are merely data. An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claim 34 recites a “wherein” clause or limitation that further limits the abstract idea of the Independent Claim 25 and contains the additional elements of: construction of a first permutation of keys for the first behavior sorted list and a second permutation of keys for the second sorted list and matching top N keys from the first permutation of keys and the second permutation of keys. This imitation recites a mental process that is practically performed in the human mind or with pen and paper because it requires mere “observation, evaluation, judgment, and/or opinion” to construct a first and second permutation of keys in any known way. The matching top N keys from the first permutation of keys and the second permutation of keys is also a mental process that is practically performed in the human mind or with pen and paper because collecting and comparing known information are steps that can be practically performed in the human mind under Classen. An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Conclusion
Claims 25–44 are therefore drawn to ineligible subject matter as they are directed to an abstract idea without significantly more. The analysis above applies to all statutory categories of invention. As such, the presentment of Rep. Claim 25 otherwise styled as another statutory category is subject to the same analysis.
Examiner Statement of Prior Art—No Prior Art Rejections
Based on the prior art search results, the prior art of record fails to anticipate or render obvious the claimed subject matter of the instant application without using the claims as a roadmap and applying improper hindsight. Independently the claims are obvious, however, the claims as a whole are not obvious because the examiner would have to improperly use the claims as a road map to improperly combine the individual obvious prior art elements together. It is noted that the priority date for the claims has support to the application filing date of Mar. 18, 2016. Rep. Claim 25 is representative of all Independent Claims. The prior art most closely resembling the applicant’s claimed invention are:
Zoldi et al. (U.S. Pat. Pub. No. 2010/0228580) is pertinent because it discloses “Fraud Detection Based on Efficient Frequent-Behavior Sorted Lists [BLists]”. Title. Zoldi does not disclose generating a first threat score influenced by a second threat score generated for a second entity, storing the first threat score in two different account profiles, and an updatable first weight utilized to determine whether one or more observed behaviors are to be deleted, preserved, or replaced.
Zoldi et al. (U.S. Pat. Pub. No. 2012/0101937) s pertinent because it discloses “Fraud Detection Based on Efficient Frequent-Behavior Sorted Lists [BLists]”. Title. Zoldi does not disclose generating a first threat score influenced by a second threat score generated for a second entity, storing the first threat score in two different account profiles, and an updatable first weight utilized to determine whether one or more observed behaviors are to be deleted, preserved, or replaced.
Li et al. (U.S. Pat. Pub. No. 2010/0036672) is pertinent because it discloses generating a first threat score influenced by a second threat score generated for a second entity. Fig. 1. Li does not disclose storing the first threat score in two different account profiles and an updatable first weight utilized to determine whether one or more observed behaviors are to be deleted, preserved, or replaced.
FOR: WO 2011/068797 A1 is pertinent because it discloses determining one or more behavioral baseline (normal risk) scores, each score associated with one or more behaviors and based on financial institution data from multiple financial institutions, monitoring financial institution data to determine deviations from the behavioral baseline (normal risk) score(s), determining a risk score based on risk patterns associated with the data, and transmitting alerts. Abstract. FOR does not disclose storing the first threat score in two different account profiles, and an updatable first weight utilized to determine whether one or more observed behaviors are to be deleted, preserved, or replaced.
NPL: Tang, Fang, “CS240 Lecture Notes on Recursion,” https://www.cpp.edu/~ftang/courses/CS240/lectures/recursion.htm is pertinent because it discloses the basics of the well-known technique of recursion.
NPL: Dr. Daisy Fang's Home Page https://www.cpp.edu/~ftang/ is pertinent because it discloses the background knowledge of a PHOSITA in recursion.
NPL: Course Syllabus CS240 https://www.cpp.edu/~ftang/courses/CS240/ is pertinent because it discloses that recursion is a subject learned in the sophomore year of a typical, college-level, computer science curriculum.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES H MILLER whose telephone number is (469)295-9082. The examiner can normally be reached M-F: 10- 4 PM (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bennett M Sigmond can be reached at (303) 297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES H MILLER/ Primary Examiner, Art Unit 3694
1 Statements of intended use fail to limit the scope of the claim under BRI. MPEP § 2103(I)(C).
2 “It should be noted that these groupings are not mutually exclusive, i.e., some claims recite limitations that fall within more than one grouping or sub-grouping.” MPEP § 2106.04(a).
3 “It should be noted that these groupings ar e not mutually exclusive, i.e., some claims recite limitations that fall within more than one grouping or sub-grouping.” MPEP § 2106.04(a).
4 Lindemann Maschinenfabrik GMBH v. Am. Hoist & Derrick Co., 730 F.2d 1452, 1463 (Fed. Cir. 1984) (citing In re Meyers, 410 F.2d 420, 424 (CCPA 1969) (“[T]he specification need not disclose what is well known in the art”).
5 See Changes in Examination Procedure Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility Decision (Berkheimer v. HP, Inc.), 3-4, https://www.uspto.gov/sites/default/files/documents/memo-berkheimer-20180419.PDF (April, 18, 2018) (That additional elements are well-understood, routine, or conventional may be supported by various forms of evidence, including "[a] citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s).").