DETAILED ACTION
1. This office action is in response to the Application No. 18634350 filed on 02/26/2026. Claims 8 and 20 have been cancelled. Claims 1-7, 9-19 and 21-24 are presented for examination and are currently pending. Applicant’s arguments have been carefully and respectfully considered.
Response to Arguments
2. The claim amendments of 02/26/2026 has overcome the 112(b) and 112(a) of the last office action. As a result, the 112 rejections has been withdrawn.
However, the claim amendments of 02/26/2026 has not overcome the 101 rejection of last office action. As a result, the 101 rejection is maintained and adjusted to reflect the amended claims.
On page 12 of the remarks, the Applicant argued that “As the specification describes, the invention avoids occurrence for false positive and false negative identification. This is principally achieved via Applicant's obtaining of the recited tags that identify commonality between a TA and WE. The achievement can, as was discussed in the recent interview with the Office, be performed according to high frequency, minimal interval processing which, as will be understood, cannot be performed with accuracy in the human mind or otherwise by thus far known alternative implementations. As was also explained in the interview, the ability for determination of or termination of determination of the recited tags inherently fosters such high frequency, minimal processing such that processing for unworthy candidacy determination can be stopped, thus making more efficient processing capability for candidacy determination requests that merit such determination. In this regard, and with similarity to Example 47 of the Guidance due to the aforementioned processing of the tags, it is respectfully submitted that integration and/or significantly more is provided relative to Applicant's recitation of, "the obtaining the watchlist candidacy (being) contingent on the obtaining one or more watchlist tags defining that the one or more watchlist tags include a comparison that the TA and the WE share at least the predetermined offense as a common identity characteristic, whereas if the TA and the WE do not share at least the predetermined offense as 8 common identity characteristic, the watchlist candidacy is not obtained, and the obtaining the watchlist candidacy is terminated." As such, it is respectfully requested that the rejection under Section 101 be withdrawn”.
The above argument is not persuasive because the arguments are directed towards abstract ideas. For instance, the obtaining of watchlist candidacy which is based on comparing whether the TA and the WE share a predetermined offense as a common identity characteristics are analyzed as abstract ideas in the office action.
In addition, the argument that the invention has the ability for determination of or termination of determination of the recited tags inherently fosters such high frequency and minimal processing is not persuasive. This is because these improvement is provided from the abstract ideas/judicial exception. It is important to note that judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.
As a result, the claimed invention is ineligible.
Applicant’s arguments are moot in view of the new grounds of rejection. The Examiner is withdrawing the rejections in the previous office action because the applicant amendments necessitated new grounds of rejection presented in this office action.
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.
3. Claims 1-7, 9-19 and 21-24 are rejected under 35 U.S.C 101 because the claimed invention is directed towards an abstract idea without significantly more.
Step 1
Independent claim 1 is directed to a method, and falls into one of the four statutory categories.
Step 2A, Prong 1
Claim 1 recites the following abstract ideas:
determining watchlist candidacy in real time(Mental process directed to determining a watchlist candidacy in real time which can be done with a pen and paper), the method comprising:
determining, based on (a} the identity characteristics corresponding to the TA (b} the identity characteristics corresponding to the WE, a respective collective identity of at least the TA (Mental process directed to determining a collective identity of a transaction applicant (TA) based on identity characteristics. This process can be practically performed in the human mind by evaluating the identity characteristics),
wherein the determining (Mental process directed to determining a collective identity of a transaction applicant (TA)) comprises
to decide whether at least a corresponding contextual sentiment, of the at least one of the plurality of textual AID, correlates to a predetermined offense, and in response to a correlation being decided, including one or more items of the at least one of the plurality of textual AID as part of the respective collective identity of at least the TA (Mental process directed to deciding a sentiment (i.e., positive or negative) which correlates with an offense. This process can be performed in the mind by observing the sentiment and making a judgement on deciding if there is a correlation to the offense),
wherein the correlation to the predetermined offense is decided based on whether the contextual sentiment comprises (i) a negative polarity, based on one or more descriptions provided by the at least one of the plurality of textual AID, that corresponds to the predetermined offense or (ii) a positive polarity, based on the one or more descriptions provided by the at least one of the plurality of textual AID, that does not correspond to the predetermined offense (Mental process directed to deciding a correlation to an offense the contextual sentiment comprises either a positive or negative polarity that is based on whether the descriptions provided by textual AID correspond to the predetermined offense or not. This can be done by making an observation about the contextual sentiment and making a judgement whether the descriptions provided by textual AID correspond to the predetermined offense or not);
converting at least the collective identity of the TA into first input for a first machine learning model (Mental process directed to making observations on the characteristics and representing them with values);
converting the one or more watchlist tags into second input for a second machine learning model (Mental process directed to making observations on the characteristics and representing them with values); and
wherein the obtaining the watchlist candidacy is contingent on the obtaining one or more watchlist tags defining that the one or more watchlist tags include a comparison that the TA and the WE share at least the predetermined offense as a common identity characteristic, whereas if the TA and the WE do not share at least the predetermined offense as a common identity characteristic, the watchlist candidacy is not obtained, and the obtaining the watchlist candidacy is terminated (Mental process directed to comparing whether the TA and the WE share at least the predetermined offense as a common identity characteristic which can be done by observing the common identity characteristic between the TA and the WE and making a judgement on whether the watchlist candidacy is obtained).
Step 2A, Prong 2
Claim 1 recites the following additional elements:
receiving identity characteristics corresponding to a transaction applicant (TA) comprising an applicant in a transaction for which an identity of the applicant can be detected, receiving identity characteristics corresponding to a watchlist entity (WE) comprising an individual listed on a watchlist identifying individuals possessing a propensity for malevolent action (This limitation is directed to insignificant extra solution activity of mere data gathering of identity characteristics. This does not integrate the abstract idea into a practical application. See MPEP 2106.05(g));
(c) a plurality of textual aggregated identity data (AID) continually received in real time (This limitation is directed to insignificant extra solution activity of mere data gathering of aggregated identity data (AID). This does not integrate the abstract idea into a practical application. See MPEP 2106.05(g));
applying natural language processing (NLP) to at least one of the plurality of textual AID (This limitation is directed processing the input (i.e., plurality of AID) through a machine learning model (i.e., natural language processing (NLP)) to give an output (i.e., correlation). This is mere instruction to apply an exception. This does not integrate the abstract idea into a practical application. See MPEP 2106.05(f))
applying the first input to the first machine learning model (This limitation is directed to processing of data using machine learning model. This is mere instruction to apply an exception. This does not integrate the abstract idea into a practical application. See MPEP 2106.05(f)) and,
in response application of a long short-term memory (LSTM) on output from the model (This is directed to linking the use of a judicial exception to a particular technological environment or field of use. This does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)),
obtaining one or more watchlist tags comprising an identity characteristic shared by the TA and the WE, that comprises at least the predetermined offense (This limitation is directed to insignificant extra solution activity of data gathering of watchlist tags. This does not integrate the abstract idea into a practical application. See MPEP 2106.05(g));
applying the first input and the second input to at least the second machine learning model (This limitation is directed to processing of data using machine learning model. This is mere instruction to apply an exception. This does not integrate the abstract idea into a practical application. See MPEP 2106.05(f)) and,
in response, obtaining a watchlist candidacy for the TA (This limitation is directed to insignificant extra solution activity of mere data gathering of watchlist candidacy. This does not integrate the abstract idea into a practical application. See MPEP 2106.05(g)).
wherein the obtaining the watchlist candidacy is blocked in the absence of the obtaining one or more watchlist tags and the respective conversion thereof into the second input for the second machine learning model (This limitation is directed to recitation at a high-level of generality of mere instructions to apply an exception. This does not integrate the abstract idea into a practical application. See MPEP 2106.05 (f)).
Step 2B
Claim 1 recites the following additional elements:
receiving identity characteristics corresponding to a transaction applicant (TA) comprising an applicant in a transaction for which an identity of the applicant can be detected, receiving identity characteristics corresponding to a watchlist entity (WE) comprising an individual listed on a watchlist identifying individuals possessing a propensity for malevolent action (This limitation is directed to insignificant extra solution activity of mere data gathering of identity characteristics and it is well understood routine and conventional. This does not amount to significantly more than judicial exception. See MPEP 2106.05(d)(II), example i);
receiving one or more aggregated identity data (AID) corresponding to one or more of the identity characteristics corresponding to the TA and the WE (This limitation is directed to insignificant extra solution activity of mere data gathering of aggregated identity data (AID) and it is well understood routine and conventional. This does not amount to significantly more than judicial exception. See MPEP 2106.05(d) (II), example i);
(c) a plurality of textual aggregated identity data (AID) continually received in real time (This limitation is directed to insignificant extra solution activity of mere data gathering of aggregated identity data (AID) and it is well understood routine and conventional. This does not amount to significantly more than judicial exception. See MPEP 2106.05(d) (II), example i);
applying natural language processing (NLP) to at least one of the plurality of textual AID to decide correlation to a predetermined offense, and (This limitation is directed processing the input (i.e., plurality of AID) through a machine learning model (i.e., natural language processing (NLP)) to give an output (i.e., correlation). This is recited at a high-level of generality. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f))
applying the first input to the first machine learning model (This limitation is directed to processing of data using machine learning model. This is recited at a high-level of generality mere instructions to apply an exception. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)) and,
in response application of a long short-term memory (LSTM) on output from the model (This is directed to linking the use of a judicial exception to a particular technological environment or field of use. This does not amount to significantly more than judicial exception. See MPEP 2106.05(h))
obtaining one or more watchlist tags comprising an identity characteristic shared by the TA and the WE (This limitation is directed to insignificant extra solution activity mere of data gathering of watchlist tags. This does not amount to significantly more than judicial exception. See MPEP 2106.05(d));
applying the first input and the second input to at least the second machine learning model (This limitation is directed to processing of data using machine learning model. This is recited at a high-level of generality. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)) and,
in response, obtaining a watchlist candidacy for the TA (This limitation is directed to insignificant extra solution activity of mere data gathering of watchlist candidacy. This does not amount to significantly more than judicial exception. See MPEP 2106.05(d) and it is well understood routine and conventional. This does not amount to significantly more than judicial exception. See MPEP 2106.05(d) (II), example i)).
wherein the obtaining the watchlist candidacy is blocked in the absence of the obtaining one or more watchlist tags and the respective conversion thereof into the second input for the second machine learning model (This limitation is directed to recitation at a high-level of generality of mere instructions to apply an exception. This does not amount to significantly more than judicial exception. See MPEP 2106.05 (f)).
4. Dependent claim 2 is directed to a method, and falls into one of the four statutory categories.
Claim 2 do not recite any abstract ideas.
Claim 2 recites the following additional elements:
wherein: the identity characteristics corresponding to one or more of the TA and the WE comprise one or more of (a) name, (b) ethnicity, (c) date of birth, (d) residence address, (e) email address, (f) gender, (g) national identification, (h) geolocation data, or (i) any combination thereof (This limitation is directed to a particular type or source of data, which is field of use and it does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)).
Claim 2 recites the following additional elements:
wherein: the identity characteristics corresponding to one or more of the TA and the WE comprise one or more of (a) name, (b) ethnicity, (c) date of birth, (d) residence address, (e) email address, (f) gender, (g) national identification, (h) geolocation data, or (i) any combination thereof (This limitation is directed to a particular type or source of data, which is field of use and it does not amount to significantly more than judicial exception. See MPEP 2106.05(h)).
5. Dependent claim 3 is directed to a method, and falls into one of the four statutory categories.
Claim 3 do not recite any abstract ideas.
Claim 3 recites the following additional elements:
wherein: the respective collective identity of the TA comprises (j) a core identity, (k) an expressed identity, (1) social identity, (m) a government identity, or (n) any combination thereof (This limitation is directed to a particular type or source of data, which is field of use and it does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)).
Claim 3 recites the following additional elements:
wherein: the respective collective identity of the TA comprises (j) a core identity, (k) an expressed identity, (1) social identity, (m) a government identity, or (n) any combination thereof (This limitation is directed to a particular type or source of data, which is field of use and it does not amount to significantly more than judicial exception. See MPEP 2106.05(h)).
6. Dependent claim 4 is directed to a method, and falls into one of the four statutory categories.
Claim 4 do not recite any abstract ideas.
Claim 4 recites the following additional elements:
wherein: the AID comprises data that is publicly available or privately maintained and/or geolocation data (This limitation is directed to a particular type or source of data, which is field of use and it does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)).
Claim 4 recites the following additional elements:
wherein: the AID comprises data that is publicly available or privately maintained and/or geolocation data (This limitation is directed to a particular type or source of data, which is field of use and it does not amount to significantly more than judicial exception. See MPEP 2106.05(h)).
7. Dependent claim 5 is directed to a method, and falls into one of the four statutory categories.
Claim 5 do not recite any abstract ideas.
Claim 5 recites the following additional elements:
wherein: the AID comprises, based on the respective identity characteristics of the TA and/or the WE, at least (o) explicit features and/or (p) implicit features (This limitation is directed to a particular type or source of data, which is field of use and it does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)).
Claim 5 recites the following additional elements:
wherein: the AID comprises, based on the respective identity characteristics of the TA and/or the WE, at least (o) explicit features and/or (p) implicit features (This limitation is directed to a particular type or source of data, which is field of use and it does not amount to significantly more than judicial exception. See MPEP 2106.05(h)).
10. Dependent claim 6 is directed to a method, and falls into one of the four statutory categories.
Claim 6 do not recite any abstract ideas.
Claim 6 recites the following additional elements:
wherein: when the AID comprises implicit features, the implicit features are derived according to natural language processing (This limitation is directed to a particular type or source of data, which is field of use and it does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)).
Claim 6 recites the following additional elements:
wherein: when the AID comprises implicit features, the implicit features are derived according to natural language processing (This limitation is directed to a particular type or source of data, which is field of use and it does not amount to significantly more than judicial exception. See MPEP 2106.05(h)).
8. Dependent claim 7 is directed to a method, and falls into one of the four statutory categories.
Claim 7 do not recite any abstract ideas.
Claim 7 recites the following additional elements:
wherein: the first machine learning model comprises unsupervised learning (This limitation is directed to linking the use of a judicial exception to a particular technological environment or field of use of unsupervised machine learning model. This does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)).
Claim 7 recites the following additional elements:
wherein: the first machine learning model comprises unsupervised learning (This limitation is directed to linking the use of a judicial exception to a particular technological environment or field of use of unsupervised machine learning model. This does not amount to significantly more than the judicial exception, see MPEP 2106.05 (h)).
9. Dependent claim 9 is directed to a method, and falls into one of the four statutory categories.
Claim 9 recites the following abstract ideas:
training data comprising prior TA collective identities matched to corresponding watchlist tags (Mental process directed to matching prior TA collective to watchlist tags. The matching process can be practically done with a pen and paper).
Claim 9 recites the following additional elements:
wherein: the second machine learning model comprises supervised learning having (This limitation is directed to a second machine learning model as a supervised learning. This limitation is recited at a high-level of generality. This limitation does not integrate the judicial exception into a practical application. See MPEP 2106.05(f))
Claim 9 recites the following additional elements:
wherein: the second machine learning model comprises supervised learning having (This limitation is directed to a second machine learning model as a supervised learning. This limitation is recited at a high-level of generality. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f))
10. Dependent claim 10 is directed to a method, and falls into one of the four statutory categories.
Claim 10 recites the following abstract ideas:
determining whether the feedback is accurate according to the collective identity of the TA (Mental process directed to determining if a feedback is accurate. This can be done by checking the collective identity of the TA. This is a process that can be performed with a pen and paper which include processes that can be performed in the mind.);
based on the determining, updating at least the second machine learning model (Mental process directed to adjusting the machine learning model based on the whether the feedback is accurate. This process can be performed in the mind).
Claim 10 recites the following additional elements:
reporting the watchlist candidacy to a requester thereof (This limitation is directed to insignificant extra solution activity of data transmission to the watchlist candidacy. This limitation does not integrate the judicial exception into a practical application);
receiving feedback on the reported watchlist candidacy (This limitation is directed to insignificant extra solution activity of mere data gathering of feedback of watchlist candidacy. This does not integrate the abstract idea into a practical application),
Claim 10 recites the following additional elements:
reporting the watchlist candidacy to a requester thereof (This limitation is directed to insignificant extra solution activity of data transmission to the watchlist candidacy and it is well understood routine and conventional. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (d)(II), example i);
receiving feedback on the reported watchlist candidacy (This limitation is directed to insignificant extra solution activity of mere data gathering of feedback of watchlist candidacy and it is well understood routine and conventional. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (d)(II), example i),
11. Dependent claim 11 is directed to a method, and falls into one of the four statutory categories.
Claim 11 do not recite ant abstract ideas.
Claim 11 recites the following additional elements:
wherein: the watchlist candidacy comprises a probability that the identity of the TA matches the identity of the WE (This limitation is directed to a particular type or source of data, which is field of use. This does not integrate the abstract idea into a practical application, see MPEP 2106.05 (h)).
Claim 11 recites the following additional elements:
wherein: the watchlist candidacy comprises a probability that the identity of the TA matches the identity of the WE (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (h)).
12. Dependent claim 12 is directed to a method, and falls into one of the four statutory categories.
Claim 12 do not recite ant abstract ideas.
Claim 12 recites the following additional elements:
wherein: the obtained watchlist candidacy is employed in connection with an identity monitoring service (This limitation is directed to a particular type or source of data, which is field of use. This does not integrate the abstract idea into a practical application, see MPEP 2106.05 (h)).
Claim 12 recites the following additional elements:
wherein: the obtained watchlist candidacy is employed in connection with an identity monitoring service (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (h)).
13. Independent claim 13 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 13, it is substantially similar to claim 1, and is rejected in the same manner and reasoning applying.
Furthermore, claim 13 recite additional elements “one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to perform a process comprising:” (this limitation is directed to using a generic computer component (i.e., processor) to apply/execute the judicial exception. This does not integrate the abstract idea into a practical application nor amount to significantly more than the judicial exception, see MPEP 2106.05 (h))
14. Dependent claim 14 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 14, it is substantially similar to claim 2, and is rejected in the same manner and reasoning applying.
15. Dependent claim 15 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 15, it is substantially similar to claim 3, and is rejected in the same manner and reasoning applying.
16. Dependent claim 16 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 16, it is substantially similar to claim 4, and is rejected in the same manner and reasoning applying.
17. Dependent claim 17 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 17, it is substantially similar to claim 5, and is rejected in the same manner and reasoning applying.
18. Dependent claim 18 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 18, it is substantially similar to claim 6, and is rejected in the same manner and reasoning applying.
19. Dependent claim 19 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 19, it is substantially similar to claim 7, and is rejected in the same manner and reasoning applying.
20. Dependent claim 21 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 21, it is substantially similar to claim 9, and is rejected in the same manner and reasoning applying.
21. Dependent claim 22 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 22, it is substantially similar to claim 10, and is rejected in the same manner and reasoning applying.
22. Dependent claim 23 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 23, it is substantially similar to claim 11, and is rejected in the same manner and reasoning applying.
23. Dependent claim 24 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 24, it is substantially similar to claim 12, and is rejected in the same manner and reasoning applying.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
24. Claims 1-7, 9-19 and 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over Alkhalili et al. ("Investigation of applying machine learning for watch-list filtering in anti-money laundering." iEEE Access 9 (2021): 18481-18496) in view of Li et al. ("Identity matching using personal and social identity features." Information Systems Frontiers 13.1 (2011): 101-113) and further in view of Larson et al. (US20200366671).
Regarding claim 1, Alkhalili teaches a method of determining watchlist candidacy in real time (To this end, we propose a novel automated model for monitoring money transactions by applying ML on the watchlist filtering process and sanctions screening, pg. 18482, left col., last para.),
the method comprising: in real time (The monitoring phase is the first phase of the proposed ML Component in which the coming transactions are monitored silently, pg. 18488, left col., section A. Monitoring Phase), performing each of receiving identity characteristics (Name, AKA … Fig. 6, left col.; The following example in Figure 6 explains the message flow in the integration of ML-Component with the watch-list filtering system …, pg. 18487, left col., last para.) corresponding to a transaction applicant (TA) (Name: Mikel George, AKA: Maikel Jeorje, Fig. 6, left col.,; Transaction info: Maik William Jeorge, Table 1, pg. 18484) comprising an applicant in a transaction for which an identity of the applicant can be detected (The AML software for watch-list filtering is implemented bidirectionally; at sender and receiver institutions. Every party is responsible for validating the transaction information. The AML software implements a string matching algorithm that verifies the transaction information such as names, aka, addresses, and countries against the preloaded blacklists, pg. 18483, right col., last para. The Examiner notes the sender is an applicant);
receiving identity characteristics corresponding to a watchlist entity (WE) (Blacklist Entity: Maik William Jeorge, Table 1, pg. 18484; Blacklists are lists that contain blacklisted people, countries, or other types of entities. There are different types of blacklists, public blacklists that can be used by watch-list filtering system, pg. 18483, right col., second para.) comprising an individual listed on a watchlist identifying individuals possessing a propensity for malevolent action (Every list can have its structure, but usually, they have some vital information such as: Name: is the personś full name, Name type: can be the main name or Also Known As (AKA), Category: the entity type (Individual, Country, Group, etc.), pg. 18483, right col., second para.);
determining, based on (a) the identity characteristics corresponding to the TA (Name: Mikel George, AKA: Maikel Jeorje, Fig. 6, left col.; Transaction info: Maik William Jeorge, Table 1, pg. 18484), (b) the identity characteristics corresponding to the WE (Blacklist Entity: Maik William Jeorge, Table 1, pg. 18484; Blacklists are lists that contain blacklisted people, countries, or other types of entities. There are different types of blacklists, public blacklists that can be used by watch-list filtering system, pg. 18483, right col., second para.), and (c) a plurality of textual aggregated identity data (AID) continually received in real time (As part of the AML program implemented in financial institutions, transactions are validated against blacklists using a string-matching algorithm. String matching is based on an exact match or a partial match (not 100% match) between the scanned and blacklist entities (pg. 18484, left col., last para.); This will help the ML-Component to monitor coming transactions, and update the decision of the new transactions, pg. 18486, right col., last para.) for the determining, a respective collective identity of at least the TA (All of the collected information then transformed into useful applications in the data analysis layer, pg. 18486, left col., second to the last para.),
converting at least the collective identity of the TA into first input for a first machine learning model (All of the collected information then transformed into useful applications in the data analysis layer. In this layer, data cleaning is performed and then sent the result to several agents that include a neural network agent, an expert system agent, and a data mining agent to analyze, pg. 18486, left col., second to the last para.);
applying the first input to the first machine learning model (Training data → Train the ML algorithm, Fig. 7, pg. 18488. Th Examiner notes training data is derived from AML software data storage) and, in response to application of a machine learning ML model on output from the first machine learning model (Train the ML algorithm → Model, fig. 7, pg. 18488; Hence, the ML model will be more effective than rule-based systems by improving the quality of the alerts (pg. 18487, left col., second to the last para.); … it will use a portion of the transactions as training data to tune the model, pg. 18488, left col., second para. The Examiner notes Model is applied on output from train the ML algorithm, Fig. 7, pg. 18488),
obtaining one or more watchlist tags comprising an identity characteristic (Matched Rank 100%, pg. 18484, right col., Table 1), shared by the TA (Transaction Info: Maik William Jeorge, Table 1, pg. 18484) and the WE (Blacklist Entity: Maik William Jeorge, Table 1, pg. 18484), that comprises at least the predetermined offense (if the matching rank is the same or higher than the threshold value, a detection ticket will be reported, pg. 18484, left col., second full para. The Examiner notes that detection is predetermined offense);
converting the one or more watchlist tags into second input for a second machine learning model (Model → ML algorithm, Fig. 7, pg. 18488. The Examiner notes the output of Model is the second input ML algorithm which is the second machine learning model); and
applying the first input (input data → ML algorithm, Fig. 7. The examiner notes input data and training data are both retrieved from AML Software data storage) and the second input to at least the second machine learning model (Model → ML algorithm, Fig. 7, pg. 18488. The Examiner notes the output of Model is the second input into ML algorithm which is the second machine learning model) and, in response, obtaining a watchlist candidacy for the TA (ML algorithm → Evaluate, Fig. 7, pg. 18488; The watch-list filtering system can depend on the ML-Component to evaluate the pending transactions and provide the recommended decision on whether to release or reject the blocked transactions, pg. 18488, right col., second to the last para.),
Alkhalili does not explicitly teach wherein the determining comprises applying natural language processing (NLP) to at least one of the plurality of textual AID to decide whether at least a corresponding contextual sentiment, of the at least one of the plurality of textual AID, correlates to a predetermined offense, and in response to a correlation being decided, including one or more items of the at least one of the plurality of textual AID as part of the respective collective identity of at least the TA, wherein the correlation to the predetermined offense is decided based on whether the contextual sentiment comprises (i) a negative polarity, based on one or more descriptions provided by the at least one of the plurality of textual AID, that corresponds to the predetermined offense or (ii) a positive polarity, based on the one or more descriptions provided by the at least one of the plurality of textual AID, that does not correspond to the predetermined offense; applying the first input to the first machine learning model and, in response to application of a long short-term memory (LSTM), wherein the obtaining the watchlist candidacy is contingent on the obtaining one or more watchlist tags defining that the one or more watchlist tags include a comparison that the TA and the WE share at least the predetermined offense as a common identity characteristic, whereas if the TA and the WE do not share at least the predetermined offense as a common identity characteristic, the watchlist candidacy is not obtained, and the obtaining the watchlist candidacy is terminated.
Li teaches wherein the determining comprises applying machine learning ML (Identity matching techniques based on machine learning can be further categorized into distance-based and probabilistic methods (pg. 104, section 2.3.2)) to at least one of the plurality of textual AID (Figure 3 shows an example of four relational tables in the criminal database. The three identity instances (John, Tom, and Jon) in the Person table have different name, date of birth (DOB), and social security number (SSN) values, pg. 106, right col. first para.) to decide whether at least a corresponding contextual sentiment, of the at least one of the plurality of textual AID, correlates to a predetermined offense (The Participation table indicates their involved incidents and their roles in those incidents. The Incident table shows the crime type and reported time of each incident. Records in these tables are related by reference slot chains defined as foreign keys, as denoted by arrows. To determine the values of Match.exist for each identity pair in the Match table, we need to follow the slot chains to capture dependencies in the three tables: Person, Participation, and Incident, pg. 106, right col. first para.), and
in response to a correlation being decided, including one or more items of the at least one of the plurality of textual AID as part of the respective collective identity of at least the TA (In this example, high similarity scores of personal identity features (name, DOB, and SSN) between P1 (John) and P3 (Jon) suggests that they are the same person. Following the slot chains, more information about P1 and P3 can be extracted: they were both “arrestees” in two “assault” incidents (C1 and C2) reported at night (“21:40” and “20:35”); P2 (Tom) was the “victim” in both incidents. Such information reveals their identities from a social perspective and further confirms the matching decision made based on personal identity features, pg. 106, right col. first para.),
wherein the correlation to the predetermined offense is decided based on whether the contextual sentiment comprises (i) a negative polarity, based on one or more descriptions provided by the at least one of the plurality of textual AID, that corresponds to the predetermined offense or (ii) a positive polarity, based on the one or more descriptions provided by the at least one of the plurality of textual AID, that does not correspond to the predetermined offense (By extending the slot chain to the classes of Participation and Incident through foreign keys, we can probe the individual’s social activity patterns such as the crime types (e.g., rubbery, arson, and drug offence) that the criminal is often involved and the roles (e.g., suspect, arrested, and victim) that the individual often assumes in the incidents. Using the PRM notation, we denote the set of incidents that pid1 is involved as [Match. pid1].[Participation.pid]−1.iid. The roles that the target person is identified in his/her involved incidents are denoted as [Match.pid1].[Participation.pid]−1.role. [Match. pid1].[Participation.pid]−1.[Participation.iid].crimetype represents the set of crime types that pid1 was involved., Fig. 3, pg. 107, right col. second to the last para. The Examiner notes that P1: John and P3: Jon are the same person with the same DOB arrested for assault, Fig. 3 which is a negative polarity);
wherein the obtaining the watchlist candidacy is contingent on the obtaining one or more watchlist tags defining that the one or more watchlist tags include a comparison that the TA and the WE share at least the predetermined offense as a common identity characteristic (we can probe the individual’s social activity patterns such as the crime types (e.g., rubbery, arson, and drug offence) that the criminal is often involved (pg. 107, right col., second para.); However, their social identity features showed that they played the same social roles (“arrested”) in the same types of crimes (“auto theft” and “drug offences”), pg. 112, left col., second para.),
whereas if the TA and the WE do not share at least the predetermined offense as a common identity characteristic, the watchlist candidacy is not obtained, and the obtaining the watchlist candidacy is terminated (The second example in Table 7 illustrates how the social identity features helped detect two matching identity records when the personal identity feature values suggested a false negative match decision. In this example, persons C and D shared the same last name (“Smith”) with different first names (“Matthew” vs. “Liss”). Their address information was not available. The classification model based on the personal identity features alone considered them as an unmatched identity pair, pg. 112, left col., second para.).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Alkhalili to incorporate the teachings of Li for the benefit of identity matching is a machine learning based approach that finds an individual in a database using one or more descriptive criteria of the person (Li, pg. 102, left col., third para.)
Modified Alkhalili does not explicitly teach wherein the determining comprises applying natural language processing (NLP) to at least one of the plurality of textual AID, applying the first input to the first machine learning model and, in response to application of a long short-term memory (LSTM),
Larson teaches wherein the determining comprises applying natural language processing (NLP) (the semantic processor(s), voice-based query processor(s), etc., parse the inputs into an internal representation (e.g., a set of tokens arranged in a suitable data structure) …, and apply the internal representation to a suitable Natural Language Processing (NLP) and/or Natural Language Understanding (NLU) ML model [0063])
to at least one of the plurality of textual AID (amount of time that the user's identity profile has existed (e.g., to detect recently established identities that are correlated with fraudulent activity [0017]),
applying the first input to the first machine learning model (input from 105A to comprising driver’s license, passport is applied to IVS server 145 comprising ML approach and deep learning approach, Fig. 1; the client system 105A is configured to operate a client application 110, which may be used to interact with the IVS 140 for identity verification services [0025]) and, application of a long short-term memory (LSTM) (In the example of FIG. 66, for simplicity of illustration, there is only one hidden layer in the NN. In some other embodiments, there can be many hidden layers. Furthermore, the NN can be implemented using some other type of topology, such … a Long Short Term Memory (LSTM) algorithm [0227])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Modified Alkhalili to incorporate the teachings of Larson for the benefit of identity verification process when attempting to obtain products or services from third party service providers (Larson [0014]).
Regarding claim 2, Modified Alkhalili teaches the method of claim 1, Alkhalili teaches wherein: the identity characteristics corresponding to one or more of the TA (Name: Mikel George, Fig. 6, left col.; Transaction info: Maik William Jeorge, Table 1, pg. 18484) and the WE comprise one or more of (a) name, (b) ethnicity, (c) date of birth, (d) residence address, (e) email address, (f) gender, (g) national identification, (h) geolocation data, or (i) any combination thereof (Blacklist Entity: Maik William Jeorge, Table 1, pg. 18484; Blacklists are lists that contain blacklisted people, countries, or other types of entities. There are different types of blacklists, public blacklists that can be used by watch-list filtering system, pg. 18483, right col., second para.).
Regarding claim 3, Modified Alkhalili teaches the method of claim 1, Alkhalili teaches wherein: the respective collective identity of the TA comprises (j) a core identity, (k) an expressed identity, (1) social identity, (m) a government identity, or (n) any combination thereof (public blacklists that can be used by watch-list filtering system and are published by governments … Every list can have its structure, but usually, they have some vital information such as: Name type: can be the main name or Also Known As (AKA) (pg. 18483, right col., second para.); AKA: Maikel Jeorje, Fig. 6).
Regarding claim 4, Modified Alkhalili teaches the method of claim 1, Alkhalili teaches wherein: the AID comprises data that is publicly available or privately maintained and/or geolocation data (In the watch-list filtering database, there are several useful information, either related to the transaction itself or to the blacklisted entity it matched (pg. 18489, left col., second para.); Financial institutions implement a watch-list filtering system on their operations system to assist them in monitoring financial transactions and capture any potential risk, pg. 18483, right col., last para. The Examiner notes that watch-list filtering database which is AID is publicly maintained)
Regarding claim 5, Modified Alkhalili teaches the method of claim 4, Alkhalili teaches wherein: the AID comprises, based on the respective identity characteristics of the TA and/or the WE, at least (o) explicit features and/or (p) implicit features (In Table 2, we explained the main features of the Watch-list Filtering system (pg. 18487, right col., second to the last para.); Attribute: ID, Rank, Birthyear, Status. The Examiner notes that ID, Rank, Birthyear, Status as explicit features)
Regarding claim 6, Modified Alkhalili teaches the method of claim 5, Alkhalili teaches wherein: when the AID comprises implicit features (In Table 2, we explained the main features of the Watch-list Filtering system (pg. 18487, right col., second to the last para.); Attribute: ID, Rank, Birthyear, Status. The Examiner notes that ID, Rank, Birthyear, Status as explicit features),
Larson teaches the implicit features are derived according to natural language processing (In other implementations, the NLP/NLU models may be trained on entities and intents. The entities are mappings of natural language word combinations to standard phrases conveying their unobscured meaning [0063]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Alkhalili to incorporate the teachings of Larson for the benefit of identity verification process when attempting to obtain products or services from third party service providers (Larson [0014]).
Regarding claim 7, Modified Alkhalili teaches the method of claim 1, Larson teaches wherein: the first machine learning model comprises unsupervised learning (ML algorithms build or develop ML models using … unsupervised learning (e.g., K-means clustering, principle component analysis (PCA), etc.) [0220]).
The same motivation to combine independent claim 1 applies here.
Regarding claim 9, Modified Alkhalili teaches the method of claim 1, Alkhalili teaches wherein: the second machine learning model comprises supervised learning having training data (Model → ML algorithm, Fig. 7, pg. 18488. The Examiner notes the output of Model is the second input ML algorithm which is the second machine learning model; Supervised learning: In supervised learning, there should be a set of input attributes and an output value, pg. 18484, right col., first bullet point)
comprising prior TA collective identities (All of the collected information then transformed into useful applications in the data analysis layer, pg. 18486, left col., second to the last para.) matched to corresponding watchlist tags (Matched Rank 79%, pg. 18484, right col., Table 1).
Regarding claim 10, Modified Alkhalili teaches the method of claim 9, Larson teaches further comprising: reporting the watchlist candidacy to a requester thereof (FIG. 61 shows an application report GUI instance 6100, which may be displayed upon completion of an application of a low fraud risk enrollee. The application report GUI instance 6100 includes a GCE 6125, which when selected by the interviewer using pointer V05, may send results of the enrollment application to the enrollee's client system 105A or to the SPP 120. FIG. 62 shows an application report GUI instance 6200 [0185]);
receiving feedback on the reported watchlist candidacy (The application report GUI instance 6300 includes a GCE 6325, which when selected by the interviewer using pointer V05, may cause the application dashboard GUI (see, e.g., FIG. 35) to be displayed [0185]),
determining whether the feedback is accurate according to the collective identity of the TA (In some embodiments, the collected biographic data is run against multiple attributes and/or variables to verify that the biographic information collected during the enrollment is accurate and/or to determine a probability that the enrollee identity is a synthetic identity 0018]);
based on the determining, updating at least the second machine learning model (FIG. 26 also shows another example home screen GUI instance 2610, in accordance with some embodiments. In this example, the home screen GUI instance 2610 is or acts as a member/applicant portal (e.g., the secure portal discussed previously). The portal provides an enrollee or user with the ability to update their biographic data; volunteer additional information [0134]; The learned experience may be used to produce new or updated models for determining future actions to take [0072]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Alkhalili to incorporate the teachings of Larson for the benefit of identity verification process when attempting to obtain products or services from third party service providers (Larson [0014]).
Regarding claim 11, Modified Alkhalili teaches the method of claim 1, Larson teaches wherein: the watchlist candidacy comprises a probability that the identity of the TA matches the identity of the WE (Additionally, as shown by FIG. 44, the IVS has detected that the biographic data provided by the subject enrollee matches the biographic data 4413 of the identity document [0168]; In some embodiments, the collected biographic data is run against multiple attributes and/or variables to verify that the biographic information collected during the enrollment is accurate and/or to determine a probability that the enrollee identity is a synthetic identity. Multiple other fraud risk indices are searched to determine a probability that the enrollment is a synthetic identity, an attempt to compromise a real identity, whether an identity is being intentionally manipulated [0018]).
The same motivation to combine independent claim 1 applies here.
Regarding claim 12, Modified Alkhalili teaches the method of claim 1, Larson teaches wherein: the obtained watchlist candidacy is employed in connection with an identity monitoring service (According to various embodiments, the client system 105A is configured to operate a client application 110, which may be used to interact with the IVS 140 for identity verification services [0025]; The first example identity verification service may also involve the one or more IVS servers 145 collecting biographic data of the user from one or more external sources such as, for example, governmental databases (e.g., DMV, police, FBI, electoral records, property records, utility data, etc.) [0042]).
The same motivation to combine independent claim 1 applies here.
Regarding claim 13, claim 13 is similar to claim 1. It is rejected in the same manner and reasoning applying. Further, Larson teaches one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to perform a process comprising (The one or more servers in a cloud include individual computer systems, where each of the servers include one or more processors, one or more memory devices [0080]; the AI agents may be developed using a suitable programming language, development tools/environments, etc., which are executed by one or more processors of one or more IVS servers 145. In this example, program code of the AI agents may be executed by a single processor or by individual processing devices [0073]):
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Alkhalili to incorporate the teachings of Larson for the benefit of identity verification process when attempting to obtain products or services from third party service providers (Larson [0014]).
Regarding claim 14, claim 14 is similar to claim 2. It is rejected in the same manner and reasoning applying.
Regarding claim 15, claim 15 is similar to claim 3. It is rejected in the same manner and reasoning applying.
Regarding claim 16, claim 16 is similar to claim 4. It is rejected in the same manner and reasoning applying.
Regarding claim 17, claim 17 is similar to claim 5. It is rejected in the same manner and reasoning applying.
Regarding claim 18, claim 18 is similar to claim 6. It is rejected in the same manner and reasoning applying.
Regarding claim 19, claim 19 is similar to claim 7. It is rejected in the same manner and reasoning applying.
Regarding claim 21, claim 21 is similar to claim 9. It is rejected in the same manner and reasoning applying.
Regarding claim 22, claim 22 is similar to claim 10. It is rejected in the same manner and reasoning applying.
Regarding claim 23, claim 23 is similar to claim 11. It is rejected in the same manner and reasoning applying.
Regarding claim 24, claim 24 is similar to claim 12. It is rejected in the same manner and reasoning applying.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MORIAM MOSUNMOLA GODO whose telephone number is (571)272-8670. The examiner can normally be reached Monday-Friday 8am-5pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle T Bechtold can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.G./Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148
Prosecution Insights