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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 May 26, 2026 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-18 recite a method (process), claim 19 recites a system (machine), and claim 20 recites a computer program product (manufacture) and therefore fall into a statutory category.
Step 2A – Prong 1 (Is a Judicial Exception Recited?):
Referring to claims 1-20, the claims recite a manner of predicting information (illicit activity or entity) based on the analysis of collected information, which under its broadest reasonable interpretation covers concepts covered under the Mental Processes grouping of abstract ideas.
The abstract idea portion of the claims is as follows:
Claim 1
A method, comprising: automatically determining at least one feature for a [machine learning] model including by:
receiving a graph of nodes and edges, determining a starting node in the graph including by inverting a direction of the graph to form an inverted directed acyclic graph, [executing a descendant search algorithm on the inverted directed acyclic graph to return nodes reachable from a source node] and determining that an illicit node is reachable from the starting node based on an inspection of the returned nodes such that at least one successful random walk can be obtained for the starting node;
automatically performing traversal walks on the graph from the starting node, wherein:
performing each of the traversal walks includes traversing to a randomly selected next node until any of one or more stopping criteria is met, and at least one traversal walk of the traversal walks includes a traversal of the graph; identifying a subset of the traversal walks, wherein each walk of the subset of traversal walks meets a first criterion of the one or more stopping criteria, and the first stopping criterion includes reaching an illicit node;
determining one or more metrics including a distribution of observed walk sizes of the subset of the traversal walks, wherein at least one metric of the one or more metrics is associated with the graph;
determining the at least one feature based at least in part on the determined one or more metrics, wherein the at least one feature includes a metric associated with a quantity of the subset of the traversal walks meeting the first criterion with respect to a total number of the traversal walks;
and enriching input data to a [machine learning] model including by querying the [machine learning] model using the input data and the determined at least one feature to predict that the input data is associated with an illicit activity or entity.
Claim 19
[A system, comprising: one or more processors configured to:] automatically determine at least one feature for a [machine learning] model including by being configured to:
receive a graph of nodes and edges, determine a starting node in the graph including by inverting a direction of the graph to form an inverted directed acyclic graph, [executing a descendant search algorithm on the inverted directed acyclic graph to return nodes reachable from a source node] and determining that an illicit node is reachable from the starting node based on an inspection of the returned nodes such that at least one successful random walk can be obtained for the starting node;
receive an identification of a starting node in the graph;
automatically perform traversal walks on the graph from the starting node, [wherein the one or more processors are configured to] perform each of the traversal walks including by being configured to:
traverse to a randomly selected next node until any of one or more stopping criteria is met, and at least one traversal walk of the traversal walks includes a traversal of the dense graph;
identify a subset of the traversal walks, wherein each walk of the subset of traversal walks meets a first criterion of the one or more stopping criteria, and the first stopping criterion includes reaching an illicit node;
determine one or more metrics including a distribution of observed walk sizes of the subset of the traversal walks, wherein at least one metric of the one or more metrics is associated with the dense graph;
determine the at least one feature based at least in part on the determined one or more metrics, wherein the at least one feature includes a metric associated with a quantity of the subset of the traversal walks meeting the first criterion with respect to a total number of the traversal walks;
and enrich input data to a [machine learning] model including by querying the [machine learning] model using the input data and the determined at least feature to predict that the input data is associated with an illicit activity or entity;
[and a memory coupled to at least one of the one or more processors and configured to provide at least one of the one or more processors with instructions].
Claim 20
[A computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for:] automatically determining at least one feature for a [machine learning] model including by:
receiving a graph of nodes and edges, determining a starting node in the graph including by inverting a direction of the graph to form an inverted directed acyclic graph, [executing a descendant search algorithm on the inverted directed acyclic graph to return nodes reachable from a source node] and determining that an illicit node is reachable from the starting node based on an inspection of the returned nodes such that at least one successful random walk can be obtained for the starting node;
receiving an identification of a starting node in the graph;
automatically performing traversal walks on the graph from the starting node, wherein performing each of the traversal walks includes traversing to a randomly selected next node until any of one or more stopping criteria is met, and at least one traversal walk of the traversal walks includes a traversal of the dense graph;
identifying a subset of the traversal walks, wherein each walk of the subset of traversal walks meets a first criterion of the one or more stopping criteria, and the first stopping criterion includes reaching an illicit node;
determining one or more metrics including a distribution of observed walk sizes of the subset of the traversal walks, wherein at least one metric of the one or more metrics is associated with the dense graph;
determining the at least one feature based at least in part on the determining one or more metrics, wherein the at least one feature includes a metric associated with a quantity of the subset of the traversal walks meeting the first criterion with respect to a total number of the traversal walks;
and enriching input data to a [machine learning] model including by querying the [machine learning] model using the input data and the determined at least one feature to predict that the input data is associated with an illicit activity or entity.
Where the portions not bracketed recite the abstract idea.
Here the claims recite concepts capable of being performed in the human mind and/or via pen and paper (including an observation, evaluation, judgement, opinion) but for the recitation of generic computer components. In the present application reciting concepts directed to predicting information (illicit activity or entity) based on the analysis of collected information. (See paragraphs 14-19 and 39-40).
If a claim limitation, under its broadest reasonable interpretation, covers concepts capable of being performed in the human mind and/or via pen and paper, it falls under the Mental Processes grouping of abstract ideas. See MPEP 2106.04.
Step 2A-Prong 2 (Is the Exception Integrated into a Practical Application?):
The Examiner views the following as the additional elements:
One or more processors. (See paragraph 12)
A system. (See paragraph 28)
A memory. (See paragraphs 51-52)
Instructions/computer instructions. (See paragraphs 51 and 54)
A computer program product. (See paragraph 56)
A non-transitory computer readable medium. (See paragraph 56)
Machine learning. (See paragraphs 20, 27 and 34)
These additional elements are recited at a high-level of generality such that they act to merely “apply” the abstract idea using generic computing components and do not integrate the abstract idea into a practical application. (See MPEP 2106.05 (f))
Referring to “executing a descendant search algorithm on the inverted directed acyclic graph to return nodes reachable from a source node” the examiner views as results-oriented solution steps lacking details and therefore equivalent to mere instructions to apply the abstract idea using generic computing components and do not integrate the abstract idea into a practical application. (See Id.) and paragraph 31 of the Specification).
The combination of these additional elements and/or results oriented steps are no more than mere instructions to apply the exception using generic computing components. (See MPEP 2106.05 (f). Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B (Does the claim recite additional elements that amount to Significantly More than the Judicial Exception?):
As noted above, the claims as a whole merely describes a method that generally “apply” the concepts discussed in prong 1 above. (See MPEP 2106.05 f (II)) In particular applicant has recited the computing components at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. As the court stated in TLI Communications v. LLC v. AV Automotive LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) merely invoking generic computing components or machinery that perform their functions in their ordinary capacity to facilitate the abstract idea are mere instructions to implement the abstract idea within a computing environment and does not add significantly more to the abstract idea. Accordingly, these additional computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea and as a result the claim is not patent eligible.
Dependent claims 2-7 and 9-17 further define the abstract idea as identified. Therefore claims 2-7 and 9-17 are considered to be patent ineligible.
Dependent claim 8 further defines the abstract idea as identified. Additionally, the claim recites the machine learning model (See paragraph 27) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claim 8 is considered to be patent ineligible.
Dependent claim 18 further defines the abstract idea as identified. Additionally, the claim recites the machine learning model (See paragraph 27) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claim 18 is considered to be patent ineligible.
In conclusion the claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed May 26, 2026 have been fully considered.
Applicant’s amendments and arguments on pages 7-8 of the Remarks, regarding the 101 rejection the Examiner finds unpersuasive.
Applicant argues that the amended claims recite inverting a graph direction to form an inverted directed acyclic graph (DAG) and executing a descendant search algorithm. According incapable of mentally inverting a large complex matrix representing a temporal transaction network and executing a formal DAG descendant search.
The Examiner respectfully disagrees with respect to the inversion aspect as there is no limitation on the size of the graph to suggest that a person could not invert the graph mentally or via pen and paper. The Examiner viewed the DAG descendant search as an additional element considered in the Step 2A Prong 2 and Step 2B Analysis.
Applicant argues under Step 2A Prong 2 that performing random walks on large-scale dense transaction networks presents severe computational challenges where certain nodes in a graph might have no paths to any illicit node. According to Applicant if random walks are initiated from such nodes, the computer system is unable to obtain successful random walks, which can cause the computer to become unresponsive, waste processing cycles, or get stuck in infinite. Applicant contends to address this issue a starting node can be automatically determined by first ensuring that an illicit node is reachable from the staring node by ensuring at least one successful random walk can be obtained for the starting node thereby preventing the computer from executing futile, resource-intensive traversals that lead to unresponsiveness.
The Examiner respectfully disagree because there is no recitation regarding performing random walks on large-scale dense transaction networks but rather a graph of nodes and edges. The Examiner views the determining a starting node based on ensuring that an illicit node is reachable from the staring node by ensuring at least one successful random walk can be obtained for the starting node is part of the abstract idea and does not constitute an additional element.
Appellant asserts that the concrete algorithmic operations are inherently machine-implemented and cannot be performed in the human mind or via and paper, particularly when applied transaction networks where transactions are connected based on shared common entities, where a person cannot invert a massive directed graph of transaction data and execute a descendant search algorithm to identify reachable nodes. According to Applicant utilizing specific graph-processing operations, the claimed method provides a concrete technical solution that improves the efficiency and reliability of the computer system performing the graph analysis.
The Examiner respectfully disagrees because there is no recitation limiting the graph to transaction network that is beyond a human’s capabilities as contested by Applicant. The Examiner respectfully disagrees viewing the specific graph processing operation of performing a specific algorithm amounts to mere instructions to apply the abstract idea. The Examiner views the remainder of the analysis of the graph to be capable of being performed by a person or via pen and paper and does not integrate the abstract idea into a practical application or add significantly more.
Applicant contests concrete algorithmic operations are inherently machine-implemented and cannot be performed in the human mind or via pen and paper, particularly when applied to dense transaction networks where transactions are connected based on shared common entities. A human cannot mentally invert a massive directed graph of transaction data and execute a descendant search algorithm to identify reachable nodes.
The Examiner respectfully disagrees reiterating they do not view the claims to be limited to dense transaction networks where transactions are connected based on shared common entities. The Examiner views a person may invert a graph of nodes and edges as claimed. Regarding the execution of the descendant search algorithm to identify reachable nodes, the Examiner views as a results-solution oriented step with no details in terms of how the descendant search algorithms is executed to identify reachable nodes but rather amounts to mere instructions to apply the abstract idea using generic computing components.
Therefore, for the foregoing reason the Examiner has maintained the 101 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kasperovics et al. (US 20190303505) -directed to executing a graph algorithm.
Changoly et al. (US 20050251371) – directed to graph manipulation of transactional performance data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J MONAGHAN whose telephone number is (571)270-5523. The examiner can normally be reached on Monday-Friday 8:30 am - 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached on (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J. Monaghan/Examiner, Art Unit 3629
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