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 .
Terminal Disclaimer
The terminal disclaimer filed on 12/23/2025 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent No. 11,973,772 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Amendment
This is in response to the amendments filed on 12/23/2025. Claims 1, 9, and 17 have been amended. Claims 1-20 are currently pending and have been considered below.
Response to Arguments
Applicant’s arguments, see page 8 of Remarks, filed 12/23/2025, with respect to claims 1-20 have been fully considered and are persuasive. The respective rejections under 35 U.S.C. § 102 and/or § 103 of claims 1-20 has been withdrawn.
Applicant's arguments filed 12/23/2025 have been fully considered but they are not persuasive. On page 7 of Remarks, Applicant contends that the claims “do not merely use machine learning on email”, and “the specific arrangement of a pre-filtering machine learning model preventing downstream processing constitutes significantly more than the abstract idea”. The examiner respectfully disagrees.
First, Applicant states that the limitations of a “first machine learning model” configured to “filter non-malicious emails to prevent processing by a second machine learning model” would move the claim beyond the “gathering and analyzing” steps found abstract in Electric Power Group, and further states that “It aligns the claim with Enfish, LLC … In Enfish, the court held that the claims directed to a specific improvement in the computer’s functioning are not abstract”. However, this is not found persuasive because no “specific improvement in the computer’s functioning” is recited in any of the independent claims. Instead, the claims recite a “first machine learning model … configured to filter non-malicious emails to prevent processing by a second machine learning model” and the “second machine learning model configured to determine a threat risk parameter”, however these are not tied to “a specific improvement” and instead appear directed towards conventional steps in the technology. For example, pre-filtering messages to prevent further processing is commonly performed via firewall systems, or via standard mailing operations to ensure a message being properly delivered. Another example would be a person checking their mailbox, retrieving mail, and separating junk mail from the mail. Additionally, the determination of a “threat risk parameter” based on a “second machine learning model” does not describe the specific improvement to the technology either, as no further functionality is described that occurs due to such “threat risk parameter” being determined.
Thus, the recited steps of “filter non-malicious emails to prevent processing by a second machine learning model” and “the second machine learning model configured to determine a threat risk parameter” do not recite a practical application of the abstract idea, as both of these limitations appear as either further directed to the abstract idea itself or insignificant extra-solution activity. These steps are also being performed at high level of generality via the usage of generic machine learning models, which also do not constitute as significantly more than the abstract idea. Therefore, these limitations are deemed as merely reciting the outcomes of using a “first machine learning model” and a “second machine learning model” without any further details about how the outcomes are accomplished in a manner which would be deemed either a practical application of the abstract idea or a specific improvement to the technology (See MPEP 2106.05(f)).
Second, regarding the Applicant’s further contention that the “Claims do not merely use machine learning on email”, the examiner notes that the features upon which Applicant relies (i.e., “Specific interactions between two distinct machine learning models optimize processing resources” in order to “reduce latency and improve system efficiency by load shedding”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The examiner recommends that Applicant further amend the claim to definitively recite subject matter which would 1) integrate the abstract idea into a practical application and/or 2) provide significantly more than the abstract idea itself. Without such recitations within the claims, the examiner maintains that under Broadest Reasonable Interpretation the claims are still directed towards an abstract idea without further integrating the abstract idea into a practical application or by reciting significantly more than the abstract idea itself, and thus the rejection under 35 U.S.C. § 101 is being sustained as below.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) “receiving access to emails…” (mental process - observation), “acquiring an incoming email…” (mental process - observation), applying a first machine learning model … to generate a first output indicating whether the incoming email is verified as non-malicious ” (mental process - making a determination/judgement), “extracting a primary attribute from the incoming email…” (mental process - evaluation), and “determining whether the incoming email deviates from past email activity…” (mental process - judgement). This judicial exception is not integrated into a practical application because the claims do not recite any further limitations that either apply, rely on, or utilize the abstract idea that imposes a meaningful limit on the abstract idea itself. For example, there’s no further recitation(s) of a specific improvement to computerized function nor to a technology or technical field, and the claims fail to clearly link towards any practical application found within the Specification. Specifically, the claims fail to recite subject matter that would constitute as more than insignificant extra-solution activity (i.e., “receiving” and “providing” are mere data gathering and output recited at a high level of generality), or apply certain limitations (i.e., “a first machine learning model” and “a second machine learning model) in a manner that is not merely described at a high level without further limiting how the respective first model and second model functions. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because none of claims 1, 9, and 17 recite any additional elements which would amount to significantly more than the abstract idea itself. For example, claim 9 recites “A system, comprising: a processor” and claim 17 recites “A computer program product … embodied in a non-transitory computer readable storage medium and comprising computer instructions”, which are considered typical computer components used for storing (and retrieving) information in memory, and thus are recognized as being well-understood, routine, and conventional computer functions (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). The examiner also takes Official Notice regarding the claimed “system, comprising: a processor” and “computer program product … embodied in a non-transitory computer readable storage medium and comprising computer instructions” as being well-understood and conventional in the computer arts. Further, the recitation of using “a first machine learning model” and “a second machine learning model” remain as insignificant extra-solution activity because the claims merely state “applying a first machine learning model” and “providing the secondary attribute to the second machine learning model” without further describing these functions in specific detail. Thus, the above identified abstract idea recited within claims 1, 9, and 17, when considered individually and in combination with the above recited well-known, conventional components, fails to recite subject matter that would constitute as significantly more than the abstract idea itself.
Further, dependent claims 2-8, 10-16, and 18-20 also fail to recite any further limitations that either recite a non-abstract idea, further integrate the above identified abstract into a practical application, or anything considered significantly more than the abstract idea itself. Thus, these claims area also rejected for the same reasons as applied to respective claims 1, 9, and 17 above.
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 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL B POTRATZ whose telephone number is (571)270-5329. The examiner can normally be reached on M-F 10 A.M. - 6 P.M. CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Korzuch can be reached on 571-272-7589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL B POTRATZ/Primary Examiner, Art Unit 2491