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
The instant application having Application No. 18/947,964 is presented for examination by the examiner.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 12,177,181. Although the claims at issue are not identical, they are not patentably distinct from each other because all the limitation of broader genus claims of ‘964 are contained in the narrower species claims of ‘181, as enunciated in (ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001). “A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). “ ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Claim Rejections - 35 USC § 112
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.
Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
As per claims 1, 8, and 15, the phrase “a security device/service” is unclear and can be interpreted several ways. Does the slash represent ‘or’ or ‘and’? Does the adjective security belong only to the device or the service? This is problematic because earlier, the claims introduce the term a security service. It is unclear from how the former term is defined as to whether or not a different service is required. Appropriate correction is required.
As per claims 6, 13, and 19, the term “the adding of” lacks antecedent basis because that step is only defined in claims 4, 11, and 17. Claim 6 depends from claim 3 and claims 13 and 19 depend from their respective independent claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over USP Application Publication 2011/0271341 to Satish et al., hereinafter Satish in view of NPL entitled “Attention in Recurrent Neural Networks for Ransomware Detection” by Stokes et al., hereinafter Stokes.
As per claims 1, 8, and 15, Satish teaches a processor configured to:
generate network profiles for malware samples [behavioral signatures for family of known malware; 0055];
select network signature candidates based on the network profiles [new malware sample has a behavior trace; 0058];
automatically evaluate the network signature candidates [compares trace behavior alignment relating to clusters; 0058] to automatically generate a new set of network signatures [regenerate a signature for the cluster that encompasses the new malware; 0058]; and
distribute the new set of network signatures to a security device/service to enforce the new set of network signatures to detect malware (0058); and
a memory coupled to the processor and configured to provide the processor with instructions (Fig. 1).
Satish does not explicitly teach to train a machine learning model using labeled network traffic from a security service to evaluate network events associated with a set of known malware samples and a set of known benign samples. Satish has a dataset of known malware and known benign samples (0055) but does not teach how it was derived. Stokes teaches training a machine learning model using labeled network traffic from a security service to evaluate network events associated with a set of known malware samples and a set of known benign samples (Section 4.1). Models can be trained on training data to produce a robust model that can detect and classify threats in the network. Satish could have used a trained machine learning model in order to detect new malware threats. The claim is obvious because one of ordinary skill in the art can combine methods known before the effective filing date which do not produce unpredictable results.
As per claims 2 and 9, Satish is silent in explicitly teaching the trained machine learning model corresponds to a recurrent neural network (RNN) based attention model. On the other hand, Stokes teaches the trained machine learning model corresponds to a recurrent neural network (RNN) based attention model (section 3-modifies LSTM with ARI cells having attention weights in an RNN). Stokes use of machine learning employs the above strategies because he teaches it is an improvement over RNN without attention. Satish could have used a trained machine learning model in order to detect new malware threats. The claim is obvious because one of ordinary skill in the art can combine methods known before the effective filing date which do not produce unpredictable results.
As per claims 3, 10, and 16, Satish is silent in explicitly teaching to train a machine learning model using labeled network traffic from a security service to evaluate network events associated with a set of known malware samples and a set of known benign samples; the trained machine learning model corresponds to a recurrent neural network (RNN) based attention model; the training of the machine learning model using the labeled network traffic outputs a set of attention weights, wherein the set of attention weights is associated with a corresponding set of specific network events; and an attention weight corresponds with a specific network event.
On the other hand, Stokes teaches:
to train a machine learning model using labeled network traffic from a security service to evaluate network events associated with a set of known malware samples and a set of known benign samples (section 4.1);
the trained machine learning model corresponds to a recurrent neural network (RNN) based attention model (section 3-modifies LSTM with ARI cells having attention weights in an RNN);
the training of the machine learning model using the labeled network traffic outputs a set of attention weights [recent weight matrices feed into the next cell; section 3, Long Short-term Memory, pg. 3224 column 2], wherein the set of attention weights is associated with a corresponding set of specific network events [associated with information on recent inputs for each element in the sequence]; section 3, Long Short-term Memory, pg. 3224 column 2]; and
an attention weight corresponds with a specific network event [each for each element in sequence]. Stokes use of machine learning employs the above strategies because he teaches it is an improvement over RNN without attention. For the reasons given above, it would have been obvious to use the machine learning of Stokes within the system of Satish.
As per claims 4, 11, and 17, Satish teaches the automatically evaluating of the network signature candidates to automatically generate a new set of network signatures comprises to: identify, using a set of validation network traffic (0034), the network signature candidates associated with malware, wherein a network signature candidate of the network signature candidates includes an ordered sequence of one or more network events [behavioral sequence for the new malware; 0058];
determine whether a recall rate of the network signature candidate exceeds or to equal a recall rate threshold [interpreted as the alignment rate which is associated with the edit distance that determines a similarity threshold; 0049 and 0056]; and
in response to a determination that the network signature candidate exceeds or equals the recall rate threshold, add the network signature candidate to the new set of network signatures (0051 and 0058).
As per claims 5, 12, and 18, Satish teaches a network signature candidate of the network signature candidates includes an ordered sequence of one or more network events [behavioral sequence for the new malware; 0058].
As per claims 6, 13, and 19, Satish teaches determine whether a precision rate of the network signature candidate falls below or is equal to a precision rate threshold; and in response to a determination that the network signature candidate falls below or is equal to the precision rate threshold, omit adding the network signature candidate to the new set of network signatures. [Satish teaches measuring the distance between a new behavior sequence to that of known cluster in terms of similarity (0056 and 0058). Satish only regenerates the signature for the cluster encompassing the new malware sample when the new sample was aligned close enough based on its similarity distance. Thus, it is suggested that behavior sequences outside of this threshold are not added to the new set of network signature.]
As per claims 7, 14, and 20, Satish teaches the security device/service is configured to detect malware using the new set of network signatures (0020).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed on the enclosed PTO-892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL R. VAUGHAN whose telephone number is (571)270-7316. The examiner can normally be reached on Monday - Friday, 9:30am - 5:30pm, EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynn Feild can be reached on (571) 272-2092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL R VAUGHAN/
Primary Examiner, Art Unit 2431