DETAILED ACTION
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 .
Claim Objections
Claim 1 is objected to because of the following informalities: claim 1 recites “wherein the first communication unit is configured for;”. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,395,476 in view of Kaabouch (US 2022/0272122).
Regarding claim 1, claim 1 of U.S. Patent No. 12,395,476 recites A system for facilitating secure updating of a machine learning model, the system comprising (see claim 1: “A system for facilitating secure updating of a machine learning model, the system comprising:”):
a processing device configured for (see claim 1: “a processing device configured for:”):
generating at least one request for updating a machine learning model (see claim 1: “generating at least one request for updating at least one machine learning model”); and
updating the machine learning model based on at least one update (see claim 1: “updating the at least one machine learning model based on at least one update”); and
a first encryption device communicatively coupled with the processing device, wherein the first encryption device comprises (see claim 1: “a first encryption device communicatively coupled with the processing device, wherein the first encryption device comprises”):
a first encryption unit configured for (see claim 1: “a first encryption unit configured for:”):
encrypting a native packet received from at least one device using at least one encryption key to create an encrypted egressing native packet (see claim 1: “encrypting a native packet corresponding to the at least one request received from the processing device using an encryption key to create a first encrypted native packet”);
adding a connectionless header to the first encrypted native packet to form a first egressing connectionless datagram (see claim 1: “adding a connectionless header to the first encrypted native packet to form a first egressing connectionless datagram”); and
decrypting a second encrypted native packet of an ingressing connectionless datagram using the encryption key to obtain the at least one update for the machine learning model (see claim 1: “decrypting a second encrypted native packet of an ingressing connectionless datagram using the encryption key to obtain the at least one update for the at least one machine learning model”); and
a first communication unit communicatively coupled with the first encryption unit, wherein the first communication unit is paired with a second communication unit of a second encryption device, wherein the first communication unit is configured for (see claim 1: “a first communication unit communicatively coupled with the first encryption unit, wherein the first communication unit is paired with a second communication unit of a second encryption device, wherein the first communication unit is configured for:”);
adding a complex header to the first egressing connectionless datagram for forming a first packet for delivery to the second encryption device (see claim 1: “adding a complex header to the first egressing connectionless datagram for forming a first packet for delivery to the second encryption device”);
receiving a second packet comprising the second encrypted native packet and a complex header from the second encryption device (see claim 1: “receiving a second packet comprising the second encrypted native packet and a complex header from the second encryption device”);
removing the complex header from the second packet (see claim 1: “removing the complex header from the second packet”); and
adding a connectionless header to the second packet for forming the ingressing connectionless datagram, wherein the ingressing connectionless datagram comprises the second encrypted native packet (see claim 1: “adding a connectionless header to the second packet for forming the ingressing connectionless datagram, wherein the ingressing connectionless datagram comprises the second encrypted native packet”); and
at least one communication interface configured for receiving at least one signal associated with at least one external device and the first encryption device, wherein the processing device is communicatively coupled with the at least one communication interface, wherein the processing device is further configured for (see claim 1: “at least one communication interface configured for receiving at least one signal associated with at least one external device and the first encryption device, wherein the processing device is communicatively coupled with the at least one communication interface, wherein the processing device is further configured for”):
analyzing the at least one signal using a plurality of machine learning models (emphasis added to show the difference between claim 1 of U.S. Patent No. 12,395,476 and claim 1 of the instant application) (see claim 1: “analyzing the at least one signal using a set of machine learning models from a plurality of machine learning models”);
determining an attack associated with the first encryption device based on the analyzing of the at least one signal (see claim 1: “determining an attack associated with the first encryption device based on the analyzing of the at least one signal”);
generating an alert for the attack based on the determining of the attack (see claim 1: “generating an alert for the attack based on the determining of the attack”); and
generating a performance indicator for the machine learning model based on the determining of the attack, wherein the generating of the at least one request is further based on the performance indicator for the machine learning model (see claim 1: “generating a performance indicator for each of the plurality of machine learning models based on the determining of the attack, wherein the generating of the at least one request is further based on the performance indicator for each of the plurality of machine learning models”); and
a storage device communicatively coupled with the processing device, wherein the storage device is configured for storing the plurality of machine learning models (emphasis added to show the difference between claim 1 of U.S. Patent No. 12,395,476 and claim 1 of the instant application) (see claim 1: “a storage device communicatively coupled with the processing device, wherein the storage device is configured for storing the plurality of machine learning models”).
Claim 1 of U.S. Patent No. 12,395,476 differs from claim 1 of the instant application in that it uses a plurality of machine learning models instead of a single machine learning model to analyze the at least one signal in order to determine an attack associated with the first encryption device.
However, Kaabouch discloses analyzing the at least one signal using a machine learning model (see [0042]: “Method 700 may include sending 770 the plurality of autonomous vehicle signal characteristics to an autonomous vehicle attack machine learning (ML) system. The autonomous vehicle attack ML system may include an autonomous vehicle attack ML model trained based on previously received autonomous vehicle attack signals. Method 700 may include receiving 775 a plurality of ML signal characteristics from the autonomous vehicle attack ML system.”);
determining an attack associated with the first (see [0042]: “The generation of the attack determination may be further based on the plurality of ML signal characteristics”).
Both Claim 1 of U.S. Patent No. 12,395,476 and Kaabouch disclose analyzing at least one signal in order to determine an attack associated with a first device using machine learning. It would have been obvious to substitute the plurality of machine learning models used by Claim 1 of U.S. Patent No. 12,395,476 with the single machine learning model used by Kaabouch to achieve the predictable result of analyzing the at least one signal in order to determine an attack associated with the first encryption device.
Allowable Subject Matter
Claims 1-6 are allowed.
Regarding independent claim 1, although the prior art of record Wei (US 2023/0138458) teaches (see [0022] and Abstract) a system for facilitating secure updating of a machine learning model, the system comprising: a processing device configured for: generating at least one request for updating at least one machine learning model; and updating the at least one machine learning model based on at least one update; and a first encryption device communicatively coupled with the processing device (see [0016]); while the prior art of record Cline (US 11,588,798) discloses an encrypting device including an encryption unit and a communications unit, wherein paired encrypting devices allow for communication of trusted data between trusted devices over an untrusted network, and wherein data received by the encryption unit is encrypted and provided with a connectionless header for delivery to the communications unit, and wherein data received by the communications units is provided with a complex header for delivery to the paired encrypting device (see Abstract);
none of the prior art of record alone or in combination teaches at least one communication interface configured for receiving at least one signal associated with at least one external device and the first encryption device, wherein the processing device is communicatively coupled with the at least one communication interface, wherein the processing device is further configured for: analyzing the at least one signal using a machine learning model; determining an attack associated with the first encryption device based on the analyzing of the at least one signal; generating an alert for the attack based on the determining of the attack; and generating a performance indicator for the machine learning model based on the determining of the attack, wherein the generating of the at least one request is further based on the performance indicator for the machine learning model, which are limitations of claim 1.
In addition to Wei (US 2023/0138458) and Cline (US 11,588,798), the closest prior art made of record are:
A non-patent literature entitled “WHISPER: A Tool for Run-Time Detection of Side-Channel Attacks” by Mushtaq et al. (provided in the IDS dated 12/3/2025) teaches a machine learning based side-channel attack (SCA) detection tool (see Abstract). SCAs can use a variety of physical parameters, e.g., power consumption, electromagnetic radiation, memory accesses and timing patterns to extract secret keys/information. The baseline idea here is that the SCAs can exploit variations in these parameters during the execution of cryptosystems on a particular hardware and can determine the secret information used by cryptosystems based on the observed parameters (see page 1, section I. Introduction, ¶ 1).
Cheng (CN 203840359 U, provided in the IDS dated 12/3/2025) discloses (see the Office Action dated 1/29/2025) determining an attack associated with the first encryption device based on the analyzing of the at least one signal; generating an alert for the attack based on the determining of the attack.
Kaabouch (US 2022/0272122) teaches the following: Method 700 may include sending 770 the plurality of autonomous vehicle signal characteristics to an autonomous vehicle attack machine learning (ML) system. The autonomous vehicle attack ML system may include an autonomous vehicle attack ML model trained based on previously received autonomous vehicle attack signals. Method 700 may include receiving 775 a plurality of ML signal characteristics from the autonomous vehicle attack ML system. The generation of the attack determination may be further based on the plurality of ML signal characteristics ([0042]).
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Conclusion
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/ZHIMEI ZHU/Examiner, Art Unit 2495
