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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/02/2025 has been entered.
Response to Amendment
The office action is responsive to the amendment filed on 12/02/2025. The status of the claims are as follow: Claims 1, 9, 11, 14, 18 and 20 have been amended, claims 7-8, 15 and 19 have been canceled, claims 22-24 are new. Therefore, claims 1-6, 9-14, 16-18 and 20-24 are pending for examination.
Response to Arguments
Regarding the 35 U.S.C § 101 Rejection:
Applicant's further arguments see pg. 9-17 filed 12/02/2025 have been fully considered but they are not persuasive.
APPLICANT ARGUMENT:
Applicant argues, the amended claims are eligible under 35 U.S.C § 101 for the following reasons: The claims are patent eligible under Step 2A of the 2019 Guidance, specifically applicant argues, “amended claim 1 is patent eligible under Prong Two of the 2019 Guidance, because claim 1 integrates the alleged exception into a practical application” and “improves computer technology, particularly in the space of machine learning technology. As explained in paragraph [0099]”. Applicant also submits, “...the limitations (including the newly added limitations) of claim 1 should not be evaluated in a vacuum, completely separate from the recited judicial exception. Instead, the analysis should take into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application, as further emphasized in the USPTO Memo of August 4, 2025 at pages 3-4”. Additionally, applicant argues, The claims are patent eligible under Step 2B of the 2019 Guidance because amended claim 1, “recites elements that amount to significantly more than any alleged judicial exception”. Further applicant states, “the Office Action has not identified any part of the specification, any publication, or any court case stating that the claim imitations herein are "well-understand, routine, and conventional in the field" per Berkheimer. In addition, although not dispositive by itself, the absence of prior art rejections further demonstrates that the claims herein are not "well-understand, routine, and conventional in the field" per Berkheimer. For this reason alone, Applicant respectfully submits that the claims amount to significantly more than an abstract idea, and therefore should be patentable based on step 2B of the 2019 Guidance”. Lastly, applicant argues independent claims 11 and 18 (and all the dependent claims) contain similar limitations as claim 1, therefore are patent eligible for at least the same reasons discussed above in association with claim 1. Claims 22-24 are new and added without introducing new matter, thus, are deemed patent eligible for reasons similar to those discussed above with respect to their respective independent claims. Accordingly, Applicant requests reconsideration and withdrawal of the rejections under 35 U.S.C § 101.
EXAMINER RESPONSE: Examiner respectfully disagree, applicant arguments are not persuasive. Regarding the arguments of Step 2A, amended claim 1 as presented does not integrate into a practical application under the second prong of the two-prong analysis since the claimed invention do not improves the functioning of a computer or improves another technology or technical field. Rather the claim recites additional element of:
accessing first data pertaining to a plurality of first entities that have been previously associated with a predefined activity;
...at least in part by executing a clustering algorithm with the first data...
...via a Natural Language Processing (NLP) technique...
training a neural network model using the multi-dimensional matrix;
accessing second data pertaining to a plurality of second entities on a list that contains entities that have been flagged for engaging, or having engaged, in the predefined activity; and
training a regression model at least in part using the first data;
...wherein the predefined characteristic is associated with a geographical region or a language;
....wherein the geographical region is associated with the one or more matches or the language is associated with the one or more matches, and wherein each of the alerts corresponds to a respective match of the one or more matches that indicates further investigation; and
causing a deployment of one or more resources usable to handle one or more of the alerts having the predefined characteristic.
That merely recites the words "apply it" (or an equivalent) with the judicial exception, as discussed in MPEP § 2106.05(f), adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) and generally links the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h) which the courts have identified such limitations do not integrate a judicial exception into a practical application (see MPEP 2106.04(d)(I)).
Furthermore, amended claim 1 when viewed as whole does not recite "An improvement in the functioning of a computer, or an improvement to other technology or technical field" (See MPEP 2106.05 (a)). The claim invention as presented does not disclose any technical details or feature of improvements to the technology or technological field, rather, the improvement is recited as a mental process identifying subsets of entities, generating multi-dimensional matrices and predicting whether scanning the data for matching will cause alerts in a generic manner which is an abstract idea. Therefore, the claimed invention fails to disclose any technical improvements instead discloses an abstract idea as an improvements or inventive concepts. Moreover, applicant is reminder that while “the claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”)”, if the specification sets forth an improvement in technology or a technical field, the claims must “includes the components or steps of the invention that provide the improvement described in the specification” (see MPEP § 2106.04(d)(1)).
Regarding the arguments of Step 2B, “recites elements that amount to significantly more than any alleged judicial exception”, examiner respectfully disagree. The amended claims as presented include limitations that the courts have identified not to be enough to qualify as “significantly more” when recited in a claim with a judicial exceptions this includes: adding the words “apply it” (or equivalent) with the judicial exception; simply appending well-understood, routine, conventional activities previously known to the industry, specified at high level of generality, to the judicial exception; adding insignificant extra solution activity to the judicial exception and generally linking the use of the judicial exception to a particular environment or field of use (see MPEP 2106.05 (I)(A)).
Furthermore, examiner respectfully disagree “the Office Action has not identified any part of the specification, any publication, or any court case stating that the claim imitations herein are "well-understand, routine, and conventional in the field", as in the Final Rejection dated 10/29/2025, Section: Claims Rejection -35 U.S.C § 101, the examiner provided for each claims rejection under 35 U.S.C § 101, STEP 2B, citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II). For example, in claim 1 of the Final Rejection dated 10/29/2025 examiner noted under STEP 2B, claim 1 did not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of: accessing first data pertaining to a plurality of first entities that have been previously associated with a predefined activity, accessing second data pertaining to a plurality of second entities on a list that contains entities that have been flagged for engaging, or having engaged, in the predefined activity where directed to well understood, routine of storing and retrieving information in memory, for which the “the courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity” (see MPEP 2106.05(d)(II)).
Therefore for the above reason, claims 1-6, 9-14, 16-18 and 20-24 are not directed to patent-eligible subject matter under 35 U.S.C § 101.
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 1-6, 9-14, 16-18 and 20-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-6, 9-10 and 22-23 are a method type claim. Claims 11-17 are a system claim. Claims 18, 20-21 and 24 are a non-transitory machine-readable medium type claim. Therefore, claims 1-6, 9-14, 16-18 and 20-24 are directed to either a process, machine, manufacture or composition of matter.
Regarding claim 1: 2A Prong 1:
identifying, (mental process – of identifying a subset of entities that have meet a threshold can be performed by the human mind with the help of pen and paper (e.g., evaluation)).
generating,(mental process – of generating a multi-dimensional matrix can be performed by the human mind with the help of pen and paper, for example a human can generate the multi-dimensional matrix based on analysis or evaluation of the information(e.g., evaluation)).
forecasting, (mental process – of forecasting the total volume of the alerts can be performed by the human mind with the help of pen and paper (e.g., judgment & evaluation )).
calculating a first score based on the forecasted total volume; (mental process – of calculating a first score based on the forecasted total volume can be performed by the human mind with the help of pen and paper (e.g., evaluation )).
determining a correlation between attributes of the subset of the first entities and attributes of the second data; (mental process – of determining correlation between attributes can be performed by the human mind with the help of pen and paper (e.g., evaluation )).
calculating a second score based on the correlation; (mental process – of calculating a second score based on the correlation can be performed by the human mind with the help of pen and paper (e.g., evaluation )).
predicting, at least in part based on the first score and the second score, whether scanning the second data against a plurality of third entities for one or more matches will cause a number of the alerts to exceed a predefined threshold, wherein the geographical region is associated with the one or more matches or the language is associated with the one or more matches, and wherein each of the alerts corresponds to a respective match of the one or more matches that indicates further investigation; and (mental process – of predicting based on the scores whether scanning the data for match will cause alerts can be performed by the human mind with the help of pen and paper (e.g., judgment and evaluation )).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
accessing first data pertaining to a plurality of first entities that have been previously associated with a predefined activity; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
...at least in part by executing a clustering algorithm with the first data,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...via a Natural Language Processing (NLP) technique,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
training a neural network model using the multi-dimensional matrix; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
accessing second data pertaining to a plurality of second entities on a list that contains entities that have been flagged for engaging, or having engaged, in the predefined activity; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
training a regression model at least in part using the first data; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...at least in part based on an output of the regression model,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...wherein the predefined characteristic is associated with a geographical region or a language; (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
....wherein the geographical region is associated with the one or more matches or the language is associated with the one or more matches, and wherein each of the alerts corresponds to a respective match of the one or more matches that indicates further investigation; and (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
causing a deployment of one or more resources usable to handle one or more of the alerts having the predefined characteristic (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
accessing first data pertaining to a plurality of first entities that have been previously associated with a predefined activity; ( This is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
...at least in part by executing a clustering algorithm with the first data,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...via a Natural Language Processing (NLP) technique,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
training a neural network model using the multi-dimensional matrix; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
accessing second data pertaining to a plurality of second entities on a list that contains entities that have been flagged for engaging, or having engaged, in the predefined activity; ( This is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
training a regression model at least in part using the first data; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...at least in part based on an output of the regression model,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...wherein the predefined characteristic is associated with a geographical region or a language; (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
....wherein the geographical region is associated with the one or more matches or the language is associated with the one or more matches, and wherein each of the alerts corresponds to a respective match of the one or more matches that indicates further investigation; and (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
causing a deployment of one or more resources usable to handle one or more of the alerts having the predefined characteristic (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 11: 2A Prong 1:
forecasting, (mental process – of forecasting the total volume of the alerts can be performed by the human mind with the help of pen and paper (e.g., judgment & evaluation )).
calculating a first score using (mental process – of calculating a first score using the data pertaining to the list of actors can be performed by the human mind with the help of pen and paper (e.g., evaluation)).
calculating a second score using network model; (mental process – of calculating a second score using the data pertaining to the list of actors can be performed by the human mind with the help of pen and paper (e.g., evaluation)).
calculating a weighted score based on the first score and the second score; and (mental process – of calculating a weight scores based on the first and second scores can be performed by the human mind with the help of pen and paper (e.g., evaluation)).
predicting, based on the weighted score, whether scanning the incoming list of actors against a list of users for matches will cause a number of the matches to exceed a predefined threshold; and (mental process – of predicting whether scanning the data for match will cause alerts can be performed by the human mind with the help of pen and paper (e.g., judgement)).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A system, comprising: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
accessing data pertaining to an incoming list of actors that are currently flagged for engaging in one or more predefined activities... (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
...wherein the accessed data comprises extracted attributes of entities that changed between the incoming list and a prior version of the incoming list; (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
accessing historical data pertaining to a plurality of actors that have previously engaged in the one or more predefined activities,... (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
...wherein the historical data comprises historical data associated with a predefined geographical region or with a predefined language; (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
performing a first machine learning process at least in part by using a regression model trained on the historical data; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...at least in part based on an output of the first machine learning process,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...the trained regression model and... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
performing a second machine learning process at least in part by using a neural network model trained on the historical data; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
executing one or more fraud-prevention actions based on the predicting (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A system, comprising: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
accessing data pertaining to an incoming list of actors that are currently flagged for engaging in one or more predefined activities... ( This is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
...wherein the accessed data comprises extracted attributes of entities that changed between the incoming list and a prior version of the incoming list; (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
accessing historical data pertaining to a plurality of actors that have previously engaged in the one or more predefined activities,... ( This is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
...wherein the historical data comprises historical data associated with a predefined geographical region or with a predefined language; (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
performing a first machine learning process at least in part by using a regression model trained on the historical data; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...at least in part based on an output of the first machine learning process,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...the trained regression model and... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
performing a second machine learning process at least in part by using a neural network model trained on the historical data; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
executing one or more fraud-prevention actions based on the predicting (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 18: 2A Prong 1:
identifying, at least in part by executing a clustering algorithm on the first data, a subset of the first entities that have met a predefined criterion, wherein the subset of the first entities have attributes that are in textual format; (mental process – of identifying a subset of entities that have meet a threshold can be performed by the human mind with the help of pen and paper (e.g., evaluation)).
generating a multi-dimensional matrix having a plurality of vectors, (mental process – of generating a multi-dimensional matrix can be performed by the human mind with the help of pen and paper, for example a human can draw the multi-dimensional matrix (e.g., evaluation)).
determining, based on the second data, a list of entities that changed between the incoming list and a prior version of the incoming list; (mental process – of determining a list of entities that changed between the incoming list and a prior version of the incoming list based on the second data can be performed by the human mind with the help of pen and paper (e.g., judgment and evaluation)).
forecasting, ...a total volume of alerts,... (mental process – of forecasting the total volume of the alerts can be performed by the human mind with the help of pen and paper (e.g., judgment & evaluation )).
calculating a first score based on the list of entities that changed (mental process – of calculating a first score based on the list of entities that changed and the forecasted total volume can be performed by the human mind with the help of pen and paper (e.g., evaluation )).
calculating a second score based on the list of entities that changed (mental process – of calculating a second score based on the list of entities that changed can be performed by the human mind with the help of pen and paper (e.g., evaluation )).
predicting, at least in part based the first score and the second score, whether scanning the second data against a plurality of third entities for matches will cause a number of the alerts to exceed a predefined threshold, wherein each of the alerts corresponds to a match that indicates further investigation; and (mental process – of predicting whether scanning the data for match will cause alerts can be performed by the human mind with the help of pen and paper (e.g., judgement)).
facilitating a deployment of one or more resources based on the predicting, wherein the one or more resources are usable to investigate the number of alerts (mental process – of facilitating a deployment of one or more resources based on the predicting such that the one or more resources can be used to investigate the number of alerts can be performed by the human mind with the help of pen and paper (e.g., evaluation & judgement). For example, a human can assist in planning a deployment/allocation/assignment of needed resources (e.g., additional personnel “who are proficient in the language associated with the spikes, and/or route more computing resources”, see paragraph [0011],[0018], [0031] and [0091] of the instant application) to investigate the alerts such that it possible to identify the bad actor customers/users with better accuracy).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
accessing first data pertaining to a plurality of first entities that have been previously associated with a flagged activity; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
...wherein the vectors are obtained by executing a word2vec algorithm... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
training a Convolutional Neural Network (CNN) model with the multi-dimensional matrix; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
accessing second data pertaining to an incoming list that contains a plurality of second entities that have been flagged for engaging, or having engaged, in the flagged activity; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
training a regression model with the first data; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...at least in part based on an output of the regression model,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...wherein each of the alerts is associated with a predefined geographical location or with a predefined language; (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
... and the trained regression model... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
... and the trained CNN model; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
accessing first data pertaining to a plurality of first entities that have been previously associated with a flagged activity; ( This is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
...wherein the vectors are obtained by executing a word2vec algorithm... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
training a Convolutional Neural Network (CNN) model with the multi-dimensional matrix; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
accessing second data pertaining to an incoming list that contains a plurality of
second entities that have been flagged for engaging, or having engaged, in the flagged activity; ( This is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
training a regression model with the first data; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...at least in part based on an output of the regression model,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...wherein each of the alerts is associated with a predefined geographical location or with a predefined language; (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
... and the trained regression model... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
... and the trained CNN model; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 2: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein: the predefined activity comprises a flagged activity; (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
the accessing the second data comprises obtaining the list of the second entities from an aggregator; and (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
the third entities are current users of a service provider (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 3: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the clustering algorithm comprises a K-means algorithm (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
Regarding claim 4: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the subset of the first entities comprises first entities that have previously generated matches with the third entities that exceeded the predefined threshold (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
Regarding claim 5: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the attributes are in textual format, and... (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
...wherein the multi-dimensional matrix is generated by applying a word2vec algorithm as the NLP technique to convert the attributes in textual format into the vectors of the multi- dimensional matrix (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 6: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the training the neural network model comprises running the multi-dimensional matrix through successive Convolutional Neural Network (CNN) model layers (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 9: 2A Prong 1:
wherein the prediction is based on a sum of the first score and the second score exceeding a predefined score (mathematical concept – of performing summation of two numbers in order to generate a prediction (e.g., mathematical calculation)).
2A Prong 2 and 2B: None.
Regarding claim 10: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
further comprising: tuning the neural network model via a feedback loop (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 12: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the historical data comprises historical data of actors that, when scanned against the list of users, caused the number of the matches to exceed the predefined threshold (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 13: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein: the regression model is a Gradient Boosting model (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
Regarding claim 14: 2A Prong 1:
wherein: the accessing the data pertaining to the incoming list of actors further comprises extracting a number of entities that changed between the incoming list and the prior version of the list; and (mental process – of extracting a number of entities that changed between the incoming list and a prior version of the list can be performed by the human mind with the help of pen and paper (e.g., judgment )).
the calculating the first score further comprises predicting a number of the matches by inputting the extracted number of the entities that changed into the trained regression model, wherein the first score is calculated based on the predicted number of the matches (mental process – of calculating a first score can be performed by the human mind with the help of pen and paper (e.g., evaluation)).
2A Prong 2 and 2B: None.
Regarding claim 16: 2A Prong 1:
generating a multi-dimensional matrix of vectors, the vectors being associated with attributes of the actors that have previously engaged in the one or more predefined activities; (mental process – of generating a multi-dimensional matrix can be performed by the human mind with the help of pen and paper, for example a human can draw the multi-dimensional matrix (e.g., evaluation)).
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the neural network model comprises a Convolutional Neural Network (CNN) model... (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
and wherein the neural network model is further trained based on: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
and passing the multi-dimensional matrix through successive layers of the CNN model (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 17: is rejected under the same rational of claim 5. Claim 17 only recites the additional elements of The System which is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f).
Regarding claim 20: 2A Prong 1:
wherein the predicting is performed at least in part by comparing a sum of the first score and the second score with a predefined threshold score (mathematical concept – of performing summation of two numbers in order to generate a prediction (e.g., mathematical calculation)).
2A Prong 2 and 2B: none.
Regarding claim 21: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the one or more resources comprise personnel who speak the predefined language (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
Regarding claim 22:2A Prong 1:
wherein the first score and the second score are weighted differently (mental process – of weighting the scored differently can be performed by the human mind with the help of pen and paper (e.g., evaluation and judgment)).
2A Prong 2 and 2B: None.
Regarding claim 23:
2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the predefined activity comprises a fraudulent activity, a hacking attack, a carding attack, a phishing activity, or a spamming activity (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
Regarding claim 24:
2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the operations further comprise adjusting the CNN model via a feedback loop that is based on the number of the alerts (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Claims Allowable Over Prior Art
The following is a statement of reasons for the indication of allowable subject matter:
Interpreting the claims in light of the specification examiner find the claimed invention is patentably distinct from prior art of record. The prior art does not expressly teach or render obvious the invention as recited in claims 1-6, 9-14, 16-18 and 20-24.
Rao et al. US 2020/0356994 A1 (hereinafter Rao) teaches a system and method for reducing false positive. Rao teaches performing profile entity matching based on a list of malicious entities utilizing a machine learning model to determine a likelihood that a match profile is connected to the entities based on enriched information that’s being enriched based on common attributes that are shared among the entities within the list. Thus, Rao is utilizing a list of malicious entity to identify profiles from the profile list not to identify a subset from the list of entities using clustering algorithm as claimed in the present application.
Park et al. US 2021/0064935 A1 (hereinafter Park) discloses a method for triple verification for “inferring missing paths in a knowledge graph and improving the quality and performance of a knowledge graph-based service” ([0004]). In addition, Park discloses a method for generating a matrix by vectorizing each of the entities applying word2vec ([0007]) and teaches applying Convolution Neural Network to the matrix ([0008]). However, Park does not explicitly suggest or teach using entities attributes to generate the matrix which is required by the present application.
Foster et al. US 2019/0068632 A1 (hereinafter Foster) teaches a method for malicious social media account verification. This is achieved by scanning data pertaining to social entities to identify entities associated with the scanned data, such that a “risk score” for each entities is assigned, in order to determine if an entities is a security risk based on the assigned “risk score” and the analyzation of the data pertaining to the social entities. Therefore, Foster is related to risk analysis rather than predicting if a number of matches will exceed a threshold as claimed by the present application.
Finkelshtein et al. US 20210397669 A1 (hereinafter Finkelshtein) is related to clustering web pages address for website analysis. Specifically, Finkelshtein teaches utilizing a clustering algorithm, such as K-means to perform the clustering aspect. Though, Finkelshtein discloses the K-means algorithm, Finkelshtein does not disclose identifying subset of entities that met a threshold as disclosed by the present application. Rather, Finkelshtein generally relates to website analysis and more particular “clustering web pages of a website to map the website application for use in improving fraud detection techniques and for use in other activities” ([0001]).
Butler et al. US 11,276,023 B1 (hereinafter Butler) discloses machine learning optimization for fraud detection, specifically, Butler teaches a system comprising a plurality of machine learning prediction models used to determine whether or not a transaction is likely to be fraudulent. Butler models are being trained with transactional data (i.e., a vector describing a particular transaction) that represents or does not represent fraud (col.4:56-60) and is used as input to the models to generate confidence scores which are sent to a “combiner” that produce a weighted average/prediction (col.4:60-64). Even though, Butler teaches multiple models, Butler do not disclose utilizing list of actors to make the predictions as disclosed by the present application, rather Butler utilizes transactional data.
Bridges et al. (hereinafter Bridges) teaches “Setting the threshold for high throughput detector”. Specifically Bridges, provides a method for setting thresholds in cyber operations in order to control the number of alerts being created such that their proposed work “gives new mathematical results regarding the p-value distribution. This informs an algorithm that poses an alternative—operators can accurately set the threshold of detection ensembles to bound the expected number of alerts or identify a misfit of the detection model” (pg. 1073, left col., para. 3). Thus, providing “a set of rigorous results for understanding the relationship between threshold values and alert quantities for probabilistic detectors” (Bridges, pg. 1071, Abstract lines 13-18).
The features of a method comprising “accessing first data pertaining to a plurality of first entities that have been previously associated with a predefined activity; identifying, at least in part by executing a clustering algorithm with the first data, a subset of the first entities that have met a predefined criterion; generating, via a Natural Language Processing (NLP) technique, a multi-dimensional matrix having a plurality of vectors that are associated with attributes of the subset of the first entities; training a neural network model using the multi-dimensional matrix; accessing second data pertaining to a plurality of second entities on a list that contains entities that have been flagged for engaging, or having engaged, in the predefined activity; and predicting, at least in part based on an output of the trained neural network model, whether scanning the second data against a plurality of third entities for matches will cause a number of alerts having a predefined characteristic to exceed a predefined threshold, wherein each of the alerts corresponds to a match that indicates further investigation” as disclosed in independent claim 1 as well in similar independent claim 18, and the feature of a system comprising “a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising: accessing data pertaining to an incoming list of actors that are currently flagged for engaging in one or more predefined activities; accessing historical data pertaining to a plurality of actors that have previously engaged in the one or more predefined activities; performing a first machine learning process at least in part by using a regression model trained on the historical data; calculating a first score using the trained regression model and the data pertaining to the incoming list of actors; performing a second machine learning process at least in part by using a neural network model trained on the historical data; calculating a second score using the trained neural network model and the data pertaining to the incoming list of actors; calculating a weighted score based on the first score and the second score; and predicting, based on the weighted score, whether scanning the incoming list of actors against a list of users for matches will cause a number of the matches to exceed a predefined threshold” as disclosed in independent claim 11, when taken in the context of the claim as whole, were not uncovered in the prior art teaching.
Therefore, the closest identifies prior art does not teach all the limitation of claims 1-6, 9-14, 16-18 and 20-24, even when consider in combination. Thus, claims 1-6, 9-14, 16-18 and 20-24 are allowable over the above prior art.
Conclusion
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/G.G.F./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127