Prosecution Insights
Last updated: April 19, 2026
Application No. 18/072,042

Automatic Alert Dispositioning using Artificial Intelligence

Final Rejection §101
Filed
Nov 30, 2022
Examiner
BAINS, SARJIT S
Art Unit
3623
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
BANK OF AMERICA CORPORATION
OA Round
4 (Final)
17%
Grant Probability
At Risk
5-6
OA Rounds
5y 1m
To Grant
46%
With Interview

Examiner Intelligence

Grants only 17% of cases
17%
Career Allow Rate
33 granted / 190 resolved
-34.6% vs TC avg
Strong +28% interview lift
Without
With
+28.3%
Interview Lift
resolved cases with interview
Typical timeline
5y 1m
Avg Prosecution
30 currently pending
Career history
220
Total Applications
across all art units

Statute-Specific Performance

§101
41.4%
+1.4% vs TC avg
§103
42.9%
+2.9% vs TC avg
§102
3.4%
-36.6% vs TC avg
§112
11.5%
-28.5% vs TC avg
Black line = Tech Center average estimate • Based on career data from 190 resolved cases

Office Action

§101
DETAILED ACTION Notice of Pre-AIA or AIA Status 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Notice to Applicant 2. The following is a Final Office action. In response to Examiner’s Non-Final Action of 09/17/2025, Applicant, on 12/15/2025, amended Claims 1-3, 16 and 19; cancelled Claim 7; and added new Claim 21. Claim 20 was previously cancelled and Claims 4-6, 8-15, 17 and 18 are as originally or previously presented. Claims 1-6, 8-19 and 21 are pending in this application and have been rejected below. Information Disclosure Statement 3. The information disclosure statement(s) (IDS) submitted on 10/16/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner. Response to Amendment 4. Applicant’s amendments and arguments are acknowledged. 5. Claim Objection added in light of Applicant's amendments. 6. The prior 35 USC §101 rejection of Claims maintained despite Applicant's amendments and arguments. Claim Objections 7. Claim 1 is objected to because of the following informalities: Claim 1 recites "after filtering the keywords, transform the textual notes into a text-frequency matrix" at lines 11-12, instead of "after filtering the keywords, transforming the textual notes into a text-frequency matrix". Appropriate correction is required. Claim Rejections - 35 USC § 101 8. 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. 9. Claims 1-6, 8-19 and 21 rejected under 35 U.S.C. 101 because, although they are drawn to statutory categories of method (process) or system (machine), they are also directed to a judicial exception (an abstract idea) without significantly more. 10. At Step 2A Prong One of the subject matter eligibility analysis, Claim 1 recites A method for enhanced triaging of alerts generated for customer service advisors, to reduce false positive alerts using .. dimensionality reduction before multi-label classification of textual notes about interactions between the customer service advisors and customers, the method comprising: .. based on risk management supervision notes including one or more contextual keywords and received as concatenated conversation strings, .. a .. model to predict false positive alerts .. includes: filtering keywords in textual notes; after filtering the keywords, transform[ing] the textual notes into a text-frequency matrix; and outputting the text-frequency matrix as numerical representations; .. a series of user-defined questions to output a probability of a false positive alert; .. an alert generated for a customer service advisor, the alert being for a customer with a likelihood of satisfying a first scenario from among an enumerated list of scenarios; contemporaneous with servicing of the customer, augmenting the alert .. with enhanced information including a color-coded visual indicator of a likelihood that the alert corresponding to the customer is false positive based on the .. model; receiving .. feedback provided by the customer service advisor about accuracy of the likelihood that the alert is false positive; iteratively updating, based on the feedback, the .. model, wherein the .. model: infers a likelihood that the customer was previously notified of the first scenario by clustering numerical representations of the textual notes, which represent interactions between the customer service advisor and the customer, in relation to the enumerated list of scenarios, and outputs a first multi-classification relevance score associated with the customer, wherein the first multi-classification relevance score comprises a first score corresponding to the first scenario, and wherein each score in the multi-classification relevance score is determined based on measuring a numeric distance from a corresponding numerical representation to a centroid of a corresponding cluster; and in response to determining that the first score meets a threshold value, modify .. the color-coded visual indicator .. to show that the likelihood is high that the alert corresponding to the customer is false positive; Claim 16 recites A method of .. a .. model for enhanced triaging of alerts generated for customer service advisors that are servicing customers to reduce false positive alerts, wherein interactions between a customer and one or more customer service advisors are recorded in textual notes, and wherein an alert for servicing the customer indicates a first scenario from among an enumerated list of scenarios, method comprising: .. triage alerts, .. uses .. raw text and a series of user-defined questions to output a probability of a false positive alert; filtering keywords in the textual notes to remove a first set of keywords that fail to correspond to any specific scenario among the enumerated list of scenarios; after the filtering of the keywords, transforming the textual notes into a text-frequency matrix, wherein the first set of keywords are omitted from the text-frequency matrix, and wherein each row of the text-frequency matrix represents a different textual note; reducing a dimension of the text-frequency matrix, wherein the text-frequency matrix is outputted as numerical representations; k-means clustering on the numerical representations outputted by the text-frequency matrix to produce a plurality of clusters, each cluster corresponding to a single scenario in the enumerated list of scenarios; and for each textual note, outputting .. a multi-classification relevance score that includes a quantity of scores matching a quantity of clusters in the plurality of clusters, and wherein each cluster corresponds to a scenario from the enumerated list of scenarios, and wherein each score in the multi-classification relevance score is calculated as a distance from the corresponding numerical representation to a centroid of a corresponding cluster and indicates a likelihood that the customer was previously notified of the corresponding scenario for that corresponding cluster, wherein .. in response to finding that the score in the multi-classification relevance score corresponding to the first scenario satisfies a threshold value, suppress generation of the alert for servicing the customer about the first scenario as a false positive alert; determining, based on iterative updates .. based on feedback, that the .. model has achieved a threshold confidence value; and deploying the .. model for .. disposition of alerts, wherein deploying the .. model for .. disposition of alerts includes enabling .. closure of alerts; and Claim 19 recites A system configured to triage alerts generated for customer service advisors to reduce false positive alerts, the system comprising: .. triage alerts .. uses .. raw text and a series of user-defined questions to output a probability of a false positive alert; .. store textual notes about interactions between the customer service advisors and a customer; a text-frequency matrix created by concatenating the textual notes into an unstructured conversation thread associated with the customer, wherein each row of the text- frequency matrix represents a different textual note; .. dimensionality reduction of the text-frequency matrix using principal component analysis (PCA); a .. model configured to output a multi-classification relevance score associated with the customer using the dimensionally reduced text- frequency matrix and .. soft clustering, wherein each score of the multi- classification relevance score corresponds to a unique scenario in an enumerated list of scenarios, wherein each score in the multi-classification relevance score is determined based on measuring a numerical distance to a centroid of each cluster corresponding to a scenario in the enumerated list of scenarios, and wherein the enumerated list of scenarios comprises concentration, production credit, charges, velocity, and time-weighted rate of return (TWRR); .. generate an alert including a color-coded visual indicator for the customer service advisors, the alert being for the customer with a likelihood of satisfying a scenario from among the enumerated list of scenarios ..; and .. determine that a first score in the multi-classification relevance score outputted by the .. model satisfies a threshold value, wherein the first score corresponds to a first scenario in the enumerated list of scenarios ..; and suppress .. generation of the alert for servicing the customer about the first scenario, as a false positive alert; wherein the model is deployed in response to determining, based on iterative updates to the .. model, that the .. model has achieved a threshold confidence value and wherein deploying the .. model includes enabling .. closure of alerts, which are abstract ideas of Certain Methods of Organizing Human Activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), because reducing false positive alerts for customer service advisors is a business process for mitigating economic risk, and involves commercial or legal interactions and managing interactions between people; furthermore, they are also abstract ideas of Mental Processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion), because creating cluster models of textual inputs to determine a multi-classification relevance score is a process that, under broadest reasonable interpretation, can be performed in the mind, since it involves observation, evaluation, judgment or opinion. At Step 2A Prong Two of the analysis for independent Claims 1, 16 and 19, the judicial exception (abstract idea) is not integrated into a practical application because the independent Claims, including additional elements such as machine learning, training, using heuristic labels generated by a weak labeling system, a machine learning (ML) model; programing, using an artificial intelligence sentence transformer, a transformer, wherein the transformer uses positional encoding and masked multi-head attention layers; displaying, on a graphical user interface (GUI), training and executing a machine learning (ML) model, by the ML model, a computer system that implements the ML model, automated, automatic, a RMS data store, a module, wherein the customer service advisor is an AI-chat bot, an artificial intelligence AI-bot that is configured to execute the ML model using one or more processors, individually, and in combination, when viewed as a whole, are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, and the claims do not effect a transformation or reduction of a particular article to a different state or thing. Generally linking the use of the judicial exception to a particular technological environment or field of use, as in the instant claims, is not indicative of integration into a practical application - see MPEP 2106.05(h); 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 using a computer as a tool to perform an abstract idea, as in the instant claims, is also not indicative of integration into a practical application - see MPEP 2106.05(f). At Step 2B of the analysis for independent Claims 1, 16 and 19, the independent Claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (abstract idea), because these additional elements such as those listed above, individually or in combination, do not recite anything that is beyond conventional and routine activity or use of computers (as evidenced by Figures 1 and 4 of the Drawings and paragraphs 24-30 and 69-82 of the Specification in the instant Application, and court decisions such as buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) discussed at 2106.05(d) of the MPEP), do not effect a transformation or reduction of a particular article to a different state or thing, nor do they apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular field of use or technological environment. 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 using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as in the instant claims, is not indicative of an inventive concept ("significantly more"). At Step 2A Prong One, dependent Claims 2-6, 8-15, 17, 18 and 21 incorporate (and therefore recite) the abstract idea noted in the independent Claims from which they depend, and further recite extensions of that abstract idea. At Step 2A Prong Two, dependent Claims 2-6, 8-12, 14, 15, 17, 18 and 21 do not include any additional elements beyond those included in the list above with respect to the independent Claims from which they depend. These dependent Claims therefore do not integrate the judicial exception (abstract idea) into a practical application for the same reasons as stated above at Step 2A Prong Two for the respective independent Claims. At Step 2A Prong Two, dependent Claim 13 does not integrate the judicial exception (abstract idea) into a practical application because this Claim, including additional elements such as those listed above and a NLP system, individually, and in combination, when viewed as a whole, are not an improvement to a computer or a technology, the claim does not apply the judicial exception with a particular machine, and the claim does not effect a transformation or reduction of a particular article to a different state or thing. Generally linking the use of the judicial exception to a particular technological environment or field of use, as in the instant Claim, is not indicative of integration into a practical application - see MPEP 2106.05(h); 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 using a computer as a tool to perform an abstract idea, as in the instant Claim, is also not indicative of integration into a practical application - see MPEP 2106.05(f). At Step 2B, dependent Claims 2-6, 8-12, 14, 15, 17, 18 and 21 do not include any additional elements beyond those included in the list above with respect to the independent Claims from which they depend. These dependent Claims therefore do not recite anything that is sufficient to amount to significantly more than the judicial exception for the same reasons as stated above at Step 2B for the respective independent Claims. At Step 2B, dependent Claim 13 does not include additional elements that are sufficient to amount to significantly more than the judicial exception (abstract idea), because these additional elements such as those listed above and a NLP system, individually or in combination, do not recite anything that is beyond conventional and routine activity or use of computers (as evidenced by Figures 1 and 4 of the Drawings and paragraphs 24-30 and 69-82 of the Specification in the instant Application and court decisions such as buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) discussed at 2106.05(d) of the MPEP), do not effect a transformation or reduction of a particular article to a different state or thing, nor do they apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular field of use or technological environment. 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 using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as in the instant Claim, is not indicative of an inventive concept ("significantly more"). Therefore, Claims 1-6, 8-19 and 21 are rejected under 35 U.S.C. 101 as being directed to non-eligible subject matter. See Alice Corp. v. CLS Bank International, 573__ U.S. 2014. Response to Arguments 11. Applicant's arguments filed 12/15/2025 have been fully considered, but are found not persuasive with regard to the 35 U.S.C. 101 rejection. 12. Applicant argues (at pp. 11-14) that, at Step 2A Prong One of the subject matter eligibility analysis, the amended claims do not recite but merely involve an abstract idea; and that “the steps of the amended claims cannot reasonably be performed in the human mind or with pen and paper” under the abstract idea category of Mental Processes since they recite features such as a GUI, an artificial intelligence sentence transformer, a trained machine learning model for example. Examiner respectfully disagrees. As explained in detail at paragraph 10 above in this office action, the amended claims recite abstract ideas which fall under the categories of Certain Methods of Organizing Human Activity (reducing false positive alerts for customer service advisors) and Mental Processes (creating cluster models of textual inputs to determine a multi-classification relevance score). Examiner notes that claim elements such as a GUI, an artificial intelligence sentence transformer, a trained machine learning model, are additional elements as noted at Step 2A Prong Two of the subject matter eligibility analysis. 13. Applicant argues (at p. 14) that, at Step 2A Prong Two of the subject matter analysis, the claim language is not directed to an abstract idea but integrates the abstract idea into a practical application through additional elements such as “particular devices that train and execute a machine learning model .. and ..modify visual interfaces”. Examiner respectfully disagrees. As explained in detail at paragraph 10 above in this office action, the additional (computer) elements in the instant claims are merely used as a tool to implement the abstract idea, and the claims, when taken as a whole, are therefore directed to the judicial exception and are thus ineligible for patent under 35 U.S.C. 101 (see MPEP 2106.05(f)). 14. Applicant further argues (at pp. 14-15) that “improvements are recited in the claims .. analogous to the network intrusion detection of claim 3 of Example 47” of the Subject Matter Eligibility examples. Examiner respectfully disagrees. As noted in the explanation for Claim 3 in Example 47, “the disclosed system enhances security by acting in real time to proactively prevent network intrusions”, whereas “existing systems use various detection techniques for detecting potentially malicious network packets and can alert a network administrator to potential problems”. Even if the differing fact patterns between Example 47 and the instant Application are set aside, the instant Claims are analogous to the prior existing systems, in which an alert is presented but no autonomous action is taken by the computer system; this is analogous to Claim 2 of Example 47, which is deemed ineligible under the subject matter analysis, as in the instant Claims. 15. Applicant also argues (at p. 15) that “claim 1 does not monopolize every possible mental process or method of organizing human activity”, but is limited to “particular steps relying on particular devices and performed in a particular order”, and is therefore eligible under 35 U.S.C. 101. Examiner respectfully disagrees. Examiner notes that the court was concerned not only with the monopolistic preemption of broad areas, but also with the preemption of judicial exceptions in more narrowly constrained abstract ideas. First, a claim cannot avoid the preemption concern by limiting itself to a particular technological environment. See Alice, 134 S. Ct. at 2357-58 (limiting an abstract idea to computer environment does not mitigate preemption concerns). The Supreme Court has warned that a “draftsman’s art” should not trump the prohibitions against patenting abstract ideas. See Alice Corp., 132 S.Ct. at 2359 (citing Mayo, 132 S.Ct. at 1294 (quoting Parker v. Flook, 437 U.S. 584, 593 (1978))). Second, the claim is still abstract and does not recite significantly more than the abstract idea. 16. Applicant argues moreover (at pp. 14, 16-18) that, at Step 2B of the subject matter analysis, the “The claims are necessarily rooted in technology and recite particular features, processes and devices that improve accuracy of machine learning models”, and that the amended claims therefore amount to significantly more than just the abstract idea when considered as a whole including the additional elements. Examiner respectfully disagrees. For the same reasons as enunciated above for Step 2A Prong Two, the amended claim limitations when considered as a whole including the additional elements are insufficient to amount to an inventive concept (or significantly more than the abstract idea) at Step 2B because these limitations are mere use of a computer system as a tool to implement the abstract idea, and the claims therefore remain rejected under 35 U.S. C. 101, as explained in detail at paragraph 10 above in this Office Action. The same analysis holds for Claim 14, since it does not incorporate any further additional elements than Claim 1 from which it depends. Conclusion 17. 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. 18. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Fenimore et al. (US Patent Publication US 20180089681 A1) describes a system and method for screening customer records and automatically and instantaneously suspending on-going electronic transactions with likely sanctioned entities that are being processed. Freedman et al. (US Patent Publication US 20040249650 A1) describes a system and method for capturing and analyzing customer interactions using rule based analysis of prior data. 19. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARJIT S BAINS whose telephone number is (571)270-0317. The examiner can normally be reached M-F 9:30am-6:00pm. 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, Wu Rutao can be reached on (571)272-6045. 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. /SARJIT S BAINS/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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Prosecution Timeline

Nov 30, 2022
Application Filed
Sep 30, 2024
Non-Final Rejection — §101
Dec 30, 2024
Response Filed
Apr 02, 2025
Final Rejection — §101
Jul 03, 2025
Request for Continued Examination
Jul 08, 2025
Response after Non-Final Action
Sep 13, 2025
Non-Final Rejection — §101
Nov 06, 2025
Interview Requested
Nov 18, 2025
Applicant Interview (Telephonic)
Nov 19, 2025
Examiner Interview Summary
Dec 15, 2025
Response Filed
Feb 11, 2026
Final Rejection — §101 (current)

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Prosecution Projections

5-6
Expected OA Rounds
17%
Grant Probability
46%
With Interview (+28.3%)
5y 1m
Median Time to Grant
High
PTA Risk
Based on 190 resolved cases by this examiner. Grant probability derived from career allow rate.

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