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 .
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted on 12/13/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS statement has been considered by the Examiner.
Claim Objections
Claim 19 is objected to because of the following informalities: it appears that the word is has been misspelled. 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).
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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 obviousness-type double patenting as being unpatentable over claims 1-27 of U.S. Patent No. 12131237 in view of Xian-Sheng Hua US 20160217349 (hereinafter Hua).
With respect to independent claims 1, 6 and 11 of the instant application, claims 1, 8 and 15 of U.S. Patent No. 12131237 teach all the limitations of claims 1, 6 and 11 respectively, except for: causing output, by the trained machine learning model, of a classification of at least one file associated with the second organization; however, Hua discloses: (“the classifying module 120 may compare the second label resulting from classification with the first label associated with the new positive multimedia data item. Based at least in part on identifying the misclassification, the updating module 208 may adjust at least some of the model vectors. For instance, if the classifying module 120 determines that the new positive multimedia data item is incorrectly classified as the second label, the updating module 208 may scale down the model vector associated with the second label and may scale up the model vector associated with the first label.” Hua: para. 58).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miserendino with the teaching of Hua to meet the preceding limitations. One of ordinary skill in the art would have been motivated to make such modification since such techniques were known at the time of the instant invention and would be applied to further refine the classifier (Hua para. 58).
Claim 16 of the instant application is rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 21 of U.S. Patent No. 12131237. Although the claims at issue are not identical, they are not patentably distinct from each other.
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 (i.e., changing from AIA to pre-AIA ) 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, 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 Christopher T. Symons US 20150067857 (hereinafter Symon) in view of Xian-Sheng Hua US 20160217349 (hereinafter Hua).
As per claim 1, Symons teaches: A method comprising:
receiving first information associated with a first plurality of files associated with a first organization (“The physical or external process is defined by the computing session in which data is received and/or processed or during the time in which a program is running that begins when the data is received” Symons: para. 41);
based on the first information and second information associated with a second plurality of files associated with a second organization, training a machine learning model usable by the second organization for classifying files (“The device may identify a sufficient (small) number of network flows that are normal and benign activities on their network. In training a red-teaming is performed by either the support or IT team to provide examples of a small number of attacks of the type they want to identify. For example, maybe they don't want the device to alert on probes, only on exploits, so they use a variety of exploits run against the network. These known behaviors, the normals, and the attacks, are labeled as such for the device, and the machine-learning detection model for the device is then trained either on a separate, more powerful machine, or on the device itself The deployed device then uses this model to make alerting decisions.” Symons: para. 40);
Symons does not teach; however, Hua discloses: causing output, by the trained machine learning model, of a classification of at least one file associated with the second organization (“the classifying module 120 may compare the second label resulting from classification with the first label associated with the new positive multimedia data item. Based at least in part on identifying the misclassification, the updating module 208 may adjust at least some of the model vectors. For instance, if the classifying module 120 determines that the new positive multimedia data item is incorrectly classified as the second label, the updating module 208 may scale down the model vector associated with the second label and may scale up the model vector associated with the first label.” Hua: para. 58).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Symons with the teaching of Hua to meet the preceding limitations. One of ordinary skill in the art would have been motivated to make such modification since such techniques were known at the time of the instant invention and would be applied to further refine the classifier (Hua para. 58).
As per claim 2, the rejection of claim 1 is incorporated herein. Symons does not teach; however, Hua discloses: the first information comprises a first feature vector representation of the first plurality of files, and wherein the first feature vector representation comprises non-sensitive data associated with the first organization (Hau: para. 97).
As per claim 3, the rejection of claim 1 is incorporated herein. Symons teaches: the first plurality of files is associated with a first plurality of adjudicated classifications (“In training a red-teaming is performed by either the support or IT team to provide examples of a small number of attacks of the type they want to identify. For example, maybe they don't want the device to alert on probes, only on exploits, so they use a variety of exploits run against the network.” Symons: para. 40).
As per claim 4, the rejection of claim 1 is incorporated herein. Symons teaches:
the training uses at least a portion of each of the first information and the second information to form a training data set, the method further comprising:
receiving an indication of an amount of each portion of each of the first information and the second information to be used in the training data set (“Selected semi-supervised learning may depend on the choice of features, choice of event definition, etc. In this disclosure an event is any collection of features that quantify what has occurred in a network, on a host, etc. over a specified period of time. The time period may be an n-second time window or may be based on the period in which a network connection is sustained.” Symons: para. 14).
As per claim 5, the rejection of claim 1 is incorporated herein. Symons teaches:
the first information indicates at least one of a file header property, a component of a file, or a binary sequence (Symons: para. 30).
As per claim 6, this claim defines a computing device that corresponds to the method of claim 1 and does not define beyond limitations of claim 1. Therefore, claim 6 is rejected with the same rational as in the rejection of claim 1.
As per claim 7, this claim defines a computing device that corresponds to the method of claim 2 and does not define beyond limitations of claim 2. Therefore, claim 7 is rejected with the same rational as in the rejection of claim 2.
As per claim 8, this claim defines a computing device that corresponds to the method of claim 3 and does not define beyond limitations of claim 3. Therefore, claim 8 is rejected with the same rational as in the rejection of claim 3.
As per claim 9, this claim defines a computing device that corresponds to the method of claim 4 and does not define beyond limitations of claim 4. Therefore, claim 9 is rejected with the same rational as in the rejection of claim 4.
As per claim 10, this claim defines a computing device that corresponds to the method of claim 5 and does not define beyond limitations of claim 5. Therefore, claim 10 is rejected with the same rational as in the rejection of claim 5.
As per claim 11, this claim defines a computer-readable storage medium storing computer-readable instruction that corresponds to the method of claim 1 and does not define beyond limitations of claim 1. Therefore, claim 11 is rejected with the same rational as in the rejection of claim 1.
As per claim 12, this claim defines a computer-readable storage medium storing computer-readable instruction that corresponds to the method of claim 2 and does not define beyond limitations of claim 2. Therefore, claim 12 is rejected with the same rational as in the rejection of claim 2.
As per claim 13, this claim defines a computer-readable storage medium storing computer-readable instruction that corresponds to the method of claim 3 and does not define beyond limitations of claim 3. Therefore, claim 13 is rejected with the same rational as in the rejection of claim 3.
As per claim 14, this claim defines a computer-readable storage medium storing computer-readable instruction that corresponds to the method of claim 4 and does not define beyond limitations of claim 4. Therefore, claim 14 is rejected with the same rational as in the rejection of claim 4.
As per claim 15, this claim defines a computer-readable storage medium storing computer-readable instruction that corresponds to the method of claim 5 and does not define beyond limitations of claim 5. Therefore, claim 15 is rejected with the same rational as in the rejection of claim 5.
As per claim 16, Symons teaches: A system comprising: receive first information associated with a first plurality of files associated with a first organization (“The physical or external process is defined by the computing session in which data is received and/or processed or during the time in which a program is running that begins when the data is received” Symons: para. 41);
based on the first information and second information associated with a second plurality of files associated with a second organization, train a machine learning model usable by the second organization for classifying files (“The device may identify a sufficient (small) number of network flows that are normal and benign activities on their network. In training a red-teaming is performed by either the support or IT team to provide examples of a small number of attacks of the type they want to identify. For example, maybe they don't want the device to alert on probes, only on exploits, so they use a variety of exploits run against the network. These known behaviors, the normals, and the attacks, are labeled as such for the device, and the machine-learning detection model for the device is then trained either on a separate, more powerful machine, or on the device itself The deployed device then uses this model to make alerting decisions.” Symons: para. 40);
at least one second computer device configured to:
send, to the at least one first computing device, the first information (“The physical or external process is defined by the computing session in which data is received and/or processed or during the time in which a program is running that begins when the data is received.” Symons: para. 41).
Symons does not teach; however, Hua discloses: cause output, by the trained machine learning model, of a classification of at least one file associated with the second organization (“the classifying module 120 may compare the second label resulting from classification with the first label associated with the new positive multimedia data item. Based at least in part on identifying the misclassification, the updating module 208 may adjust at least some of the model vectors. For instance, if the classifying module 120 determines that the new positive multimedia data item is incorrectly classified as the second label, the updating module 208 may scale down the model vector associated with the second label and may scale up the model vector associated with the first label.” Hua: para. 58).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Symons with the teaching of Hua to meet the preceding limitations. One of ordinary skill in the art would have been motivated to make such modification since such techniques were known at the time of the instant invention and would be applied to further refine the classifier (Hua para. 58).
As per claim 17, the rejection of claim 16 is incorporated herein. Symons does not teach; however, Hua discloses: the first information comprises a first feature vector representation of the first plurality of files, and wherein the first feature vector representation comprises non-sensitive data associated with the first organization (Hau: para. 97).
As per claim 18, the rejection of claim 16 is incorporated herein. Symons teaches: the first plurality of files is associated with a first plurality of adjudicated classifications (“In training a red-teaming is performed by either the support or IT team to provide examples of a small number of attacks of the type they want to identify. For example, maybe they don't want the device to alert on probes, only on exploits, so they use a variety of exploits run against the network.” Symons: para. 40).
As per claim 19, the rejection of claim 16 is incorporated herein. Symons teaches: the training uses at least a portion of each of the first information and the second information to form a training data set, wherein the at least one first computing device us further configured to:
receive an indication of an amount of each portion of each of the first information and the second information to be used in the training data set (“Selected semi-supervised learning may depend on the choice of features, choice of event definition, etc. In this disclosure an event is any collection of features that quantify what has occurred in a network, on a host, etc. over a specified period of time. The time period may be an n-second time window or may be based on the period in which a network connection is sustained.” Symons: para. 14).
As per claim 20, the rejection of claim 16 is incorporated herein. Symons teaches: the first information indicates at least one of a file header property, a component of a file, or a binary sequence (Symons: para. 30).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GHODRAT JAMSHIDI whose telephone number is (571)270-1956. The examiner can normally be reached 10:00-6:00.
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/GHODRAT JAMSHIDI/ Primary Examiner, Art Unit 2493