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 § 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 1-21 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 and 21 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 1-21, 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 determining that an illicit node is reachable from the starting node 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 determining that an illicit node is reachable from the starting node 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 determining that an illicit node is reachable from the starting node 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))
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, 9-17, and 21 further define the abstract idea as identified. Therefore claims 2-7, 9-17, and 21 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 are 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 December 8, 2025 have been fully considered.
Applicant’s amendments and arguments on pages 8-9 of the Remarks, regarding the 101 rejection the Examiner finds unpersuasive.
Applicant argues that the amended claims recite “determining a starting node in the graph including by determining that an illicit node is reachable from the starting node such that at least one successful random walk can be obtained for the starting node” which provides a technical solution to the technical problem of not being able to obtain successful random walks as discussed in paragraph 31. According to Applicant, “in some scenarios, some nodes in a graph might have no paths to any illicit node” and therefore would be impossible to obtain successful random walks for those nodes. Applicant contends the computer would not being able to complete a process or otherwise becomes unresponsive and unable to complete a computing task and the asserted limitation avoids such scenario by ensuring that an illicit node is reachable from the starting node.
The Examiner respectfully disagrees viewing the limitation as drafted constitutes a part of the abstract idea. The limitation as claimed can still be performed as a mental process as there is no suggestion in the limitation or elsewhere in the claim to suggest that this determination cannot be performed from observing a graph and making the said determination as part of the recited abstract idea. Indeed, as provided for by Applicant “only in some scenarios” there are no paths available however this is not claimed or how the graph is processed specifically to obtain such benefits as alleged by Applicant in a manner that would not be possible to perform as Mental Processes.
Applicant argues that dependent claim 21 is eligible for the same reasons as discussed prior. The Examiner respectfully disagrees because the limitations as drafted the Examiner views as further defining the abstract idea as claimed and therefore does not view the claim to integrate the abstract idea or add significantly more to the abstract idea.
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.
Epasto et al. (US 20200035002) – directed to encoding graphs.
Yang et al. (US 20210319329) – directed to generating knowledge graph method for relation mining.
Ogrinz et al. (US 20220138261) – directed to displaying and/or manipulating graphs.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/M.J.M./Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629