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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action in response to communication received on 9/23/2025. Claims 1-6, 8, 10-12, 14-19, 21, and 23-25 are pending in this application. The Information Disclosure Statements (IDS) filed on behalf of this case on 9/23/2025 have been considered by the Examiner.
Response to Amendment
Applicant’s amendments to claims 1 and 14 are acknowledged. Applicant’s cancellation of claims 7, 9, 13, 20, and 22 are acknowledged.
Subject Matter Overcoming the Prior Art of Record
It is noted that the claims overcome the prior art of record for at least the reasonings previously discussed in the Non-Final office Action dated 5/23/2025 (see pages 3-4).
Response to Arguments
On Remarks pages 9-13, Applicant cites parts of Applicant’s specification and then argues the claims are similar to Example 47 claim 3 steps e and f in that the technique as amended solves “the technical computing problem of reaching and obtaining operational capacity for integrity of supervised learning (i.e., avoidance of false positives and negatives), as recited. For instance, were sentiment not properly identified, such supervised learning could not, on an ongoing basis, be properly trained for forthcoming iterations of operations (see above reference to specification).”
The Examiner respectfully disagrees. In USPTO Example 47 claim 3, the claim limitations were found to not be an abstract idea, cited herein “ Limitations (d)-(f) do not recite mental processes because they cannot be practically performed in the human mind. That is, the human mind is not equipped to detect a source address associated with malicious network packets, drop the malicious network packets in real time, and block future traffic as recited in the claim. See MPEP 2106.04(a)(2), subsection III.A (discussing SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019)). As step (a) and steps (b)-(c) fall within different groupings of abstract ideas (i.e., mathematical concepts and mental processes, respectively).” The fact pattern is not the same here as the argued limitations do recite a judicial exception.
Further the present application claims do not recite (e) dropping the one or more malicious network packets in real time; and (f) blocking future traffic from the source address. Therefore the Examiner respectfully disagrees that the present application is similar to the fact pattern in Example 47.
Specifically a human activity or mental process can be obtaining information via rules that one or more watchlist tags comprise a same predicate offensive involvement determined for the final collectible identifies of the TA and the WE. The amended “to exclude” limitation recites intended use or intended result, therefore does not limit claim scope (see MPEP 2111.04), and therefore does not include limitations that are additional elements that result in a practical application or significantly more. The only additional elements that describe the rules as being “unsupervised machine learning”, “long short-term memory (LSTM) modeling and “natural language processing” merely results in apply it or generally linking it to the field of computers as discussed in the 101 rejection below.
Further as part of the Analysis USPTO Example 47 describes (e) and (f) with respect to prong two as follows “The claimed invention reflects this improvement in the technical field of network intrusion detection. Steps (d)-(f) provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. Specifically, the claim reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f). These steps reflect the improvement described in the background. Thus, the claim as a whole integrates the judicial exception into a practical application such that the claim is not directed to the judicial exception.” The Examiner has reviewed Applicant’s cited specification sections, however the sections disclose no improvement to the computer, rather Applicant’s specification merely discusses updating rules over time, which are mental process steps or methods of organizing human activities and accordingly part of the abstract idea as broadly recited in the claims and discussed in the specification. The only additional elements that describe the rules as being “unsupervised machine learning”, “long short-term memory (LSTM) modeling and “natural language processing” merely results in apply it or generally linking it to the field of computers as discussed in the 101 rejection below, which are limitations previously found by the courts to not be enough to qualify as a practical application or significantly more.
Therefore the Examiner respectfully disagrees.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-6, 8, 10-12, 14-19, 21, and 23-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claims 1 and 14, Applicant recites amendments of “to exclude positive and/or negative sentiment corresponding to the AID and/or the one or more further AID from being available to comprise the one or more watchlist tags in dependence on the one or more watchlist tags comprising a same predicate offense”.
While the Examiner does not interpret the limitations to limit claim scope, as the claims recite intended use or intended result, but does not require steps to be performed, See MPEP 2111.04, in the efforts of compact prosecution, the Examiner has examined the claims for new matter. The claims recite new matter.
Specifically, the Examiner has reviewed the cited sections on pages 9-12 Of Applicant’s arguments/Remarks. While the Examiner sees some terms or parts of the obtaining limitation as amended recited in the specification, the above limitation is not found in its entirety.
From review of the entire specification the closest recitation of the above is found on pages 23-24 of the specification specifically:
“By contrast, analysis 940 can be disregarded by WCS 100 as not available to provide watchlist tags since, at least, its negative sentiment (see 960) does not qualify for a crime corresponding to a predicate offense (i.e., AID is focused on sports).”
However this does not disclose the amended limitation “to exclude positive and/or negative sentiment corresponding to the AID and/or the one or more further AID from being available to comprise the one or more watchlist tags in dependence on the one or more watchlist tags comprising a same predicate offense.” As the specification discloses not providing watchlist tags based on negative sentiment whereas the claims recite excluding positive or and negative sentiment from being available to comprise one or more watchlist tags. Further the specification does not disclose dependence on the one or more watchlist tags comprising a same predicate offense. Therefore the claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Further claims 2-6, 8, 10-12, 15-19, 21, and 23-25 that depend off of claims 1 and 14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement, based on their dependency. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10 and 23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claims 10 and 23, based on Applicant’s amendments removing the terms other machine learning from the independent claims 1 and 14, it is unclear and indefinite now as to what the limitation “the other machine learning” references or refers back to based on Applicant’s amendments to the independent claims.
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, 8, 10-12, 14-19, 21, and 23-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-6, 8 and 10- 12 recite a process as the claims recite a method. Claims 14-19, 21, and 23-25 recite a system as the claims recite a processor and memories and instructions for performing the functions.
The claim(s) 1-6, 8, 10-12, 14-19, 21 and 23-25 recite(s) the idea of determining a bad actor (for example an entity with malicious intent) based on collecting, aggregating, and matching or comparing information to a watch list. The idea of determining a bad actor (for example an entity with malicious intent) based on collecting, aggregating, and matching or comparing information to a watch list is a mental process. Further the idea of determining a bad actor (for example an entity with malicious intent) based on collecting, aggregating, and matching or comparing information to a watch list is a fundamental economic practice or principle which is certain methods of organizing human activity. Mental processes as well as certain methods of organizing human activity are in the groupings of enumerated abstracts ideas, and hence the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims merely recite limitations that are not indicative of integration into a practical application in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Specifically as recited in the claims:
The Examiner notes that the Examiner has underlined and bolded additional elements beyond the abstract idea. Limitations that are not bolded and underlined are considered part of the abstract idea.
1. (Currently Amended) A method of determining watchlist candidacy, the method comprising:
receiving identity characteristics corresponding to a transaction applicant (TA);
receiving identity characteristics corresponding to a watchlist entity (WE);
receiving one or more aggregated identity data (AID) corresponding to one or more of the identity characteristics corresponding to the TA and the WE;
determining, based on the identity characteristics corresponding to the TA and the one or more AID corresponding to one or more of the identity characteristics corresponding to the TA and the WE, a first collective identity of the TA;
determining, based on the identity characteristics corresponding to the WE and the one or more AID corresponding to one or more of the identity characteristics corresponding to the TA and the WE, a first collective identity of the WE,
receiving, in real time and according to a predetermined schedule, one or more of (a) one or more further identity characteristics corresponding to a TA, (b) one or more further identity characteristics corresponding to the WE, (c) one or more further AID corresponding to one or more of the identity characteristics corresponding to the TA and the WE, or (d) any combination thereof,
based on one or more of (a)-(d), updating the first collective identity of the TA and/or the first collective identity of the WE,
comparing one or more of the a respective identity characteristics corresponding to the updated, first collective identity of the TA and/or one or more of the a respective identity characteristics corresponding to the updated, first collective identity of the WE to a predetermined identity characteristic threshold, whereby meeting or exceeding the predetermined threshold is determinative of whether the updated, first collective identity of the respective TA and/or the WE is a final collective identity thereof;
based on the comparing, determining the updated, first collective identity or the collective identity as of the comparing, of the respective TA and/or the WE, to be a respective final collective identity of the TA and/or the WE as of the comparing,
wherein, in response to the collective identity as of the comparing, of the respective TA and/or the WE, meeting or exceeding the predetermined threshold, the collective identity as of the comparing is determined as the respective final collective identity of the TA and/or the WE;
obtaining, via unsupervised machine learning and long short-term memory (LSTM) modeling on the final collective identities of the TA and the WE, one or more watchlist tags comprising a same predicate offense involvement determined for the final collective identities of the TA and the WE, the predicate offense involvement being determined via said unsupervised machine learning and said LSTM modeling coupled with natural language processing (NLP) at least on the AID and/or the one or more further AID, to exclude positive and/or negative sentiment corresponding to the AID and/or the one or more further AID from being available to comprise the one or more waitlist tags in dependence on the one or more waitlist tags comprising a same predicate offense;
obtaining, via other supervised machine learning on the watchlist tags and the final collective identity of the TA, a watchlist candidacy for the TA,
wherein, for the supervised machine learning on the watchlist tags and the final collective identity of the TA, one or more of (i) the identity characteristics corresponding to the WE and/or the one or more further identity characteristics corresponding to the WE, (ii) the AID corresponding to one or more of the identity characteristics corresponding to the TA and the WE and/or the one or more further AID corresponding to one or more of the identity characteristics corresponding to the TA and the WE, or (iii) any combination thereof, are assigned predetermined weightings which, in response to an evaluation of the watchlist candidacy for the TA, are configured to be varied, according to operation of the supervised machine learning on the evaluation, for one or more subsequent iterations of the obtaining a watchlist candidacy for the TA.
2. (Original) The method of claim 1, wherein: the identity characteristics corresponding to one or more of the TA and the WE comprise one or more of (e) name, (f) ethnicity, (g) date of birth, (h) residence address, (i) email address,(j) gender, (k) national identification, (1) geolocation data, or (m) any combination thereof.
3. (Original) The method of claim 1, wherein: the predetermined schedule comprises a timing of continuous receipt of one or more of (n) one or more of the identity characteristics corresponding to the TA, (o) one or more of the identity characteristics corresponding to the WE, (p) the one or more of the AID corresponding to one or more of the identity characteristics corresponding to the TA and the WE, or (q) any combination thereof, or a timing of when one or more of the identity characteristics corresponding to the TA are received.
4. (Original) The method of claim 1, wherein: the updating and the comparing are conducted in real time.
5. (Previously Presented) The method of claim 1, wherein: the predetermined identity characteristic threshold corresponds to one or more of (r) movement of the TA and/or the WE, (s) social media registration of the TA and/or the WE, (t) a threshold corresponding to one or more of the AID, or (u) any combination thereof.
6. (Original) The method of claim 1, wherein :the receiving (a)-(d), the updating, and the comparing are performed iteratively for the predetermined schedule.
7. (Canceled)
8. (Original) The method of claim 1, wherein: the one or more watchlist tags each comprise a trait and/or identity characteristic shared between the TA and the WE.
9. (Canceled)
10. (Previously Presented) The method of claim 1, further comprising: reporting the watchlist candidacy to a requester thereof; receiving feedback on the reported watchlist candidacy; determining whether the feedback is accurate according to the final collective identity of the TA; based on the determining, updating at least the other machine learning.
11. (Original) The method of claim 1, wherein: the watchlist candidacy comprises a probability that the final collective identity of the TA matches the final collective identity of the WE.
12. (Original) The method of claim 1, wherein: the obtained watchlist candidacy is employed in connection with an identity monitoring service.
13. (Canceled)
14. (Currently Amended) A computing system for determining watchlist candidacy, the computing system comprising:
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:
receiving identity characteristics corresponding to a transaction applicant (TA);
receiving identity characteristics corresponding to a watchlist entity (WE);
receiving one or more aggregated identity data (AID) corresponding to one or more of the identity characteristics corresponding to the TA and the WE;
determining, based on the identity characteristics corresponding to the TA and the one or more AID corresponding to one or more of the identity characteristics corresponding to the TA and the WE, a first collective identity of the TA;
determining, based on the identity characteristics corresponding to the WE and the one or more AID corresponding to one or more of the identity characteristics corresponding to the TA and the WE, a first collective identity of the WE,
receiving, in real time and according to a predetermined schedule, one or more of (a) one or more further identity characteristics corresponding to a TA, (b) one or more further identity characteristics corresponding to the WE, (c) one or more further AID corresponding to one or more of the identity characteristics corresponding to the TA and the WE, or (d) any combination thereof,
based on (a)-(d),
updating the first collective identity of the TA and/or the first collective identity of the WE, comparing one or more of the a respective identity characteristics corresponding to the updated, first collective identity of the TA and/or one or more of the a respective identity characteristics corresponding to the updated, first collective identity of the WE to a predetermined identity characteristic threshold, whereby meeting or exceeding the predetermined identity characteristic threshold is determinative of whether the updated, first collective identity of the respective TA and/or the WE is a final collective identity thereof;
based on the comparing, determining the updated, first collective identity or the collective identity, as of the comparing, of the respective TA and the WE to be a respective final collective identity of the TA and the WE as of the comparing, wherein, in response to the collective identity as of the comparing, of the respective TA and/or the WE, meeting or exceeding the predetermined threshold, the collective identity as of the comparing is determined as the respective final collective identity of the TA and/or the WE;
obtaining, via unsupervised machine learning and long short-term memory (LSTM) modeling on the final collective identities of the TA and the WE, one or more watchlist tags comprising a same predicate offense involvement determined for the final collective identities of the TA and the WE, the predicate offense involvement being determined via said unsupervised machine learning and said LTSM modeling coupled with natural language processing (NLP) at least on the AID and/or the one or more further AID, to exclude positive and/or negative sentiment corresponding to the AID and/or the one or more further AID from being available to comprise the one or more watchlist tags in dependence on the one or more watchlist tags comprising a same predicate offense;
obtaining, via supervised machine learning on the watchlist tags and the final collective identity of the TA, a watchlist candidacy for the TA,
wherein, for the supervised machine learning on the watchlist tags and the final collective identity of the TA, one or more of (i) the identity characteristics corresponding to the WE and/or the one or more further identity characteristics corresponding to the WE, (ii) the AID corresponding to one or more of the identity characteristics corresponding to the TA and the WE and/or the one or more further AID corresponding to one or more of the identity characteristics corresponding to the TA and the WE, or (iii) any combination thereof, are assigned predetermined weightings which, in response to an evaluation of the watchlist candidacy for the TA, are configured to be varied, according to operation of the supervised machine learning on the evaluation, for one or more subsequent iterations of the obtaining a watchlist candidacy for the TA.
15. (Original) The computing system of claim 14, wherein: the identity characteristics corresponding to one or more of the TA and the WE comprise one or more of (e) name, (f) ethnicity, (g) date of birth, (h) residence address, (i) email address,(j) gender, (k) national identification, (1) geolocation data, or (m) any combination thereof.
16. (Original) The computing system of claim 14, wherein: the predetermined schedule comprises a timing of continuous receipt of one or more of (n) one or more of the identity characteristics corresponding to the TA, (o) one or more of the identity characteristics corresponding to the WE, (p) the one or more of the AID corresponding to one or more of the identity characteristics corresponding to the TA and the WE, or (q) any combination thereof, or a timing of when one or more of the identity characteristics corresponding to the TA are received.
17. (Original) The computing system of claim 14, wherein: the updating and the comparing are conducted in real time.
18. (Previously Presented) The computing system of claim 14, wherein: the predetermined identity characteristic threshold corresponds to one or more of (r) movement of the TA and/or the WE, (s) social media registration of the TA and/or the WE, (t) a threshold corresponding to one or more of the AID, or (u) any combination thereof.
19. (Original) The computing system of claim 14, wherein: the receiving (a)-(d), the updating, and the comparing are performed iteratively for the predetermined schedule.
20. (Canceled)
21. (Original) The computing system of claim 14, wherein: the one or more watchlist tags each comprise a trait and/or identity characteristic shared between the TA and the WE.
22. (Canceled)
23. (Previously Presented) The computing system of claim 14, wherein the process further comprises: reporting the watchlist candidacy to a requester thereof; receiving feedback on the reported watchlist candidacy; determining whether the feedback is accurate according to the final collective identity of the TA; based on the determining, updating at least the other machine learning.
24. (Original) The computing system of claim 14, wherein: the watchlist candidacy comprises a probability that the final collective identity of the TA matches the final collective identity of the WE.
25. (Original) The computing system of claim 14, wherein: the obtained watchlist candidacy is employed in connection with an identity monitoring service.
As per claim 1, the claims recite limitations a human or humans could perform. Specifically a human could receive characteristic data, determine a collective identity, receive additional information according to a schedule, update the information over time, compare the information to a threshold to make a determination on a final collective, determine one or more watchlist tags based on rules, and obtain a watchlist candidacy for the TA based on rules of watchlist tags and the final collective identity.
The additional elements that the information is received in “real time”, that the rules are “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” merely results in “apply it.”
Specifically here with respect to “real time” this merely invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a generic purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide sufficiently more. Further limitations that could be performed by a human or humans that instead recite being performed in “real time” merely generally link the judicial exception to the field of computers.
As to the fact that the rules are “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” merely results in “apply it” as there are no details about a particular “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” or how the ““supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” operate to derive the information other than it is generally being used to determine information or “apply it”. The “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” are generally used to apply the abstract idea without placing any limitation how the “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” operates derive the information. These limitations recite only the idea of using “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” without details on how this is accomplished. The claim omits any details as to how the “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” solves a technical problem and instead recites only the idea of a solution or outcome, as the claims a recite result oriented solution and lack details as to how the computer performs the modifications. Further the claim invokes a generic “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” for making the recited calculation rather than purporting to improve the technology or a computer. These limitations represent no more than mere instructions to apply the judicial exception on a computer. Further this can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers (additionally see for reference July 2024 Subject Matter Eligibility Examples, in particular example 48 (also noted in the response to argument section above)).
It is noted that the amended “to exclude positive and /or negative sentiment corresponding to the AID and/or the one or more further AID from being available to comprise the one or more watchlist tags in dependent on the one or more watchlist tags comprising a same predicate offense” is interpreted to not limit claim scope as the claim limitation merely recites intended use or intended result, see MPEP 2111.04. However in the efforts of compact prosecution, the Examiner has examined this limitation with respect to 101 eligibility, here it is noted there are no additional elements beyond the abstract idea as the claims recite data collection and aggregation that does not require any additional elements like a machine or computer to be performed.
As per claim 2, these limitations further recite the type of information collected. These are all limitations a human or humans could perform. There are no additional elements beyond those previously discussed.
As per claim 3, this limitation recites continuously receiving information according to a predefined schedule. These are all limitations a human or humans could perform. There are no additional elements beyond those previously discussed.
As per claim 4, this limitation recites updating and comparing information. These are limitations a human or humans could perform. The fact that this is performed in “real time” is discussed above previously in claim 1.
As per claim 5, this limitation recites what the characteristic threshold corresponds to. These are limitations a human or humans could perform. There are no additional elements beyond those previously discussed.
As per claim 6, this limitation recites updating and comparing the performed iteratively according to a schedule, these are limitations a human or humans could perform. There are no additional elements beyond those previously discussed.
As per claim 8, this limitation recites or describes the watchlist tags that they comprise a trait shared between the TA and the WE. These are limitations a human or humans could perform. There are no additional elements beyond those previously discussed.
As per claim 10, this limitation recites reporting information, receiving feedback, determining if the feedback is accurate and updating the rules based on that. These are limitations a human or humans could perform. The additional element that the rule is “other machine learning” is discussed above previously in claim 1.
As per claim 11, this limitation merely describes the watchlist candidacy as a probability that the final collective identity of the TA matches the final collective identity of the WE. These are limitations a human or humans could perform. There are no additional elements beyond those previously discussed.
As per claim 12, this limitation merely recites that the watchlist candidacy is employed in connection with an identity monitoring service. These are limitations a human or humans could perform given the broad recitation in the claim. There are no additional elements beyond those previously discussed.
As per claim 14, the claims recite limitations a human or humans could perform. Specifically a human could receive characteristic data, determine a collective identity, receive additional information according to a schedule, update the information over time, compare the information to a threshold to make a determination on a final collective, determine one or more watchlist tags based on rules, and obtain a watchlist candidacy for the TA based on rules of watchlist tags and the final collective identity.
The additional elements that the information is received in “real time”, the functions are being performed by a “computing system” with “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:”, that the rules are “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” merely results in “apply it.”
Specifically here with respect to “real time” and by a “computing system” with “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 merely invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a generic purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide sufficiently more. Further limitations that could be performed by a human or humans that instead recite being performed in “real time” or by a “computing system” with “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:” merely generally link the judicial exception to the field of computers.
As to the fact that the rules are “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” merely results in “apply it” as there are no details about a particular “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” or how the ““supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” operate to derive the information other than it is generally being used to determine information or “apply it”. The “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” are generally used to apply the abstract idea without placing any limitation how the “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” operates derive the information. These limitations recite only the idea of using “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” without details on how this is accomplished. The claim omits any details as to how the “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” solves a technical problem and instead recites only the idea of a solution or outcome, as the claims recite result oriented solution and lack details as to how the computer performs the modifications. Further the claim invokes a generic “supervised machine learning”, “unsupervised machine learning”, “long short term memory modeling (LSTM), “natural language processing” or “other machine learning” for making the recited calculation rather than purporting to improve the technology or a computer. Therefore these limitations represent no more than mere instructions to apply the judicial exception on a computer. Further this can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers (additionally see for reference July 2024 Subject Matter Eligibility Examples, in particular example 48 (cited above in the reference to arguments section)).
It is noted that the amended “to exclude positive and /or negative sentiment corresponding to the AID and/or the one or more further AID from being available to comprise the one or more watchlist tags in dependent on the one or more watchlist tags comprising a same predicate offense” is interpreted to not limit claim scope as the claim limitation merely recites intended use or intended result, see MPEP 2111.04. However in the efforts of compact prosecution, the Examiner has examined this limitation with respect to 101 eligibility, here it is noted there are no additional elements beyond the abstract idea as the claims recite data collection and aggregation that does not require any additional elements like a machine or computer to be performed.
As per claim 15, these limitations further recite the type of information collected. These are all limitations a human or humans could perform. There are no additional elements beyond those previously discussed.
As per claim 16, this limitation recites continuously receiving information according to a predefined schedule. These are all limitations a human or humans could perform. There are no additional elements beyond those previously discussed.
As per claim 17, this limitation recites updating and comparing information. These are limitations a human or humans could perform. The fact that this is performed in “real time” is discussed previously above in claim 1.
As per claim 18, this limitation recites what the characteristic threshold corresponds to. These are limitations a human or humans could perform. There are no additional elements beyond those previously discussed.
As per claim 19, this limitation recites updating and comparing the performed iteratively according to a schedule, these are limitations a human or humans could perform. There are no additional elements beyond those previously discussed.
As per claim 21, this limitation recites or describes the watchlist tags that they comprise a trait shared between the TA and the WE. These are limitations a human or humans could perform. There are no additional elements beyond those previously discussed.
As per claim 23, this limitation recites reporting information, receiving feedback, determining if the feedback is accurate and updating the rules based on that. There are limitations a human or humans could perform. The additional element that the rule is “other machine learning” is discussed above previously in claim 1.
As per claim 24, this limitation merely describes the watchlist candidacy as a probability that the final collective identity of the TA matches the final collective identity of the WE. These are limitations a human or humans could perform. There are no additional elements beyond those previously discussed.
As per claim 25, this limitation merely recites that the watchlist candidacy is employed in connection with an identity monitoring service. These are limitations a human or humans could perform given the broad recitation in the claims. There are no additional elements beyond those previously discussed.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely recite limitations that are not indicative of an inventive concept (“significantly more”) in that the claims merely recite: (1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as detailed above with respect to the practical application step.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
a. Tayebnejad et al. (United States Patent Application Publication Number: US 2002/0161731) artificial intelligence trending system to identify users who are a bad debt risk based on other records (see abstract)
b. Madhu et al. (United States Patent Application Publication Number: US 2014/0282977) teaches determining a risk and fraud assessment based on social networking data (see abstract)
c. Chari et al. (United States Patent Application Publication Number: US 2017/0286671) teaches a system that generates a user malicious activity alert based on observed information provided by the user of the user and then when an alert is generated that information is sent to an analyst for feedback (see abstract)
d. Gribelyuk et al. (United States Patent Number: US 10,754,946) teaches using machine learning to model an entity behavior to determine credit riskiness (see abstract)
e. Kirti et al. (United States Patent Application Publication Number: US 2022/0366078) teaches a system for granting access to information based on a generated risk score and control policies (see abstract and title)
f. Saunders et al. (United States Patent Application Publication Number: US 2018/0337937) teaches a system for determining malicious behaviors based on high and low variances in data based on previous history (see abstract)
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/KIERSTEN V SUMMERS/Primary Examiner, Art Unit 3626