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
Claims 21-40 are pending in this office action.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on September 4, 2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities: the CROSS-REFERENCE TO RELATED APPLICATIONS section needs updated to reflect applications that have matured into patents. 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.
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 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 of U.S. Patent No. 11,841,947. Although the claims at issue are not identical, they are not patentably distinct from each other because both application and patent claim receiving a target file; byte values within the target file; a set of informational entropy values obtained from the target file; identifying a set of Portable Executable (PE) header values associated with the target file; calculating a probability that the target file is malicious. The patent further claims communicating a threat score based on the probability, the threat score associated with a potential threat to a user, device, or network, whereas the application claims quarantining the target file based on the probability indicating that the target file is likely malicious. It would have ben obvious to either communicate a threat score or quarantine the target since both options allow for protecting malicious content from being sent.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 21-40 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Avasarala et al. (U.S. Patent Pub. No. 2014/0090061).
Regarding claim 21, Avasarala et al. teaches a non-transitory processor-readable medium storing code representing instructions to be executed by one or more processors, the instructions comprising code to cause the one or more processors to: receive a target file via a network (paragraph 0052); extract a set of byte values from the target file (paragraph 0054); identify a frequency of each byte value from the set of byte values from the target file to calculate a set of informational entropy values associated with the target file (paragraph 0098); extract a set of Portable Executable (PE) values from the target file (paragraph 0078); calculate a probability that the target file is malicious based on the set of informational entropy values, the set of PE values and at least one characteristic of the network (paragraph 0044); and quarantine the target file based on the probability indicating that the target file is likely malicious (paragraph 0052).
Regarding claims 22, Avasarala et al. teaches wherein the at least one characteristic of the network includes at least one of a type of the network, a general security of the network, or a type of business hosting the network (paragraph 0109).
Regarding claim 23, Avasarala et al. teaches wherein the set of PE values includes a PE import vector, the instructions further comprising code to cause the one or more processors to: extract an import address table from a binary representation of the target file; and calculate the PE import vector based on hashing values from the import address table (paragraph 0036).
Regarding claim 24, Avasarala et al. teaches wherein the set of PE values includes a PE metadata vector, the instructions further comprising code to cause the one or more processors to: extract a set of numerical PE fields from a representation of the target file; and calculate the PE metadata vector based on the set of numerical PE fields (paragraph 0071).
Regarding claim 25, Avasarala et al. teaches wherein the set of PE values includes at least one of a name, an age, an author, a source, a file type, or a size (paragraph 0037).
Regarding claim 26, Avasarala et al. teaches wherein the code to cause the one or more processors to calculate the probability includes code to cause the one or more processors to calculate the probability using a trained threat model including at least one of a random forest classifier or a deep neural network (paragraph 0037).
Regarding claim 27, Avasarala et al. teaches further comprising code to cause the one or more processors to: select a trained threat model based on a type of the target file, the code to cause the one or more processors to calculate the probability includes code to cause the one or more processors to calculate the probability based on the trained threat model (paragraph 0040).
Regarding claim 28, Avasarala et al. teaches wherein the code to cause the one or more processors to identify the frequency of each byte value includes code to cause the one or more processors to identify the frequency of each byte value from the set of byte values by identifying a frequency of a range of byte values to calculate the set of informational entropy values associated with the target file (paragraph 0040).
Regarding claim 29, Avasarala et al. teaches a method, comprising: receiving a target file via a network (paragraph 0052); partitioning the target file into a plurality of byte windows (paragraph 0049); extracting a set of byte values for each byte window from the plurality of byte windows of the target file (paragraph 0054); identifying a frequency of each byte value from the set of byte values for each byte window from the plurality of byte windows to calculate a set of informational entropy values associated with the target file (paragraph 0098); calculating a probability that the target file is malicious based on the set of informational entropy values and at least one characteristic of the network (paragraph 0044); and quarantining the target file based on the probability indicating that the target file is likely malicious (paragraph 0052).
Regarding claim 30, Avasarala et al. teaches wherein the at least one characteristic of the network includes at least one of a type of the network, a general security of the network, or a type of business hosting the network (paragraph 0109).
Regarding claim 31, Avasarala et al. teaches further comprising: extracting a set of Portable Executable (PE) values from the target file, the calculating the probability that the target file is malicious being based on the set of PE values (paragraph 0054).
Regarding claim 32, Avasarala et al. teaches wherein the calculating the probability includes calculating the probability using a trained threat model including at least one of a random forest classifier or a deep neural network (paragraph 0037).
Regarding claim 33, Avasarala et al. teaches wherein the identifying includes identifying the frequency of each byte value from the set of byte values for each window from the plurality of byte windows by identifying a frequency of a range of byte values for that window (paragraph 0040).
Regarding claim 34, Avasarala et al. teaches wherein the calculating the probability includes calculating the probability based on whether a byte standard deviation value for each byte window from the plurality of byte windows is within a byte standard deviation range (paragraph 0097).
Regarding claim 35, Avasarala et al. teaches a non-transitory processor-readable medium storing code representing instructions to be executed by one or more processors, the instructions comprising code to cause the one or more processors to: receive a target file (paragraph 0052); partition the target file into a plurality of byte windows (paragraph 0049); extract a set of byte values for each byte window from the plurality of byte windows of the target file (paragraph 0054); identify a frequency of each byte value from the set of byte values for each byte window from the plurality of byte windows to calculate a set of informational entropy values associated with the target file (paragraph 0098); extract a set of Portable Executable (PE) values from the target file (paragraph 0054); calculate a probability that the target file is malicious based on the set of informational entropy values and the set of PE values (paragraph 0044); and delete the target file based on the probability indicating that the target file is likely malicious (paragraph 0064).
Regarding claim 36, Avasarala et al. teaches wherein: the code to cause the one or more processors to receive includes code to cause the one or more processors to receive the target file via a network, and the code to cause the one or more processors to calculate includes code to cause the one or more processors to calculate the probability that the target file is malicious based on at least one characteristic of the network (paragraph 0044).
Regarding claim 37, Avasarala et al. teaches wherein the set of PE values includes at least one of a name, an age, an author, a source, a file type, or a size (paragraph 0037).
Regarding claim 38, Avasarala et al. teaches wherein the set of PE values includes a PE import vector, the instructions further comprising code to cause the one or more processors to: extract an import address table from a binary representation of the target file; and calculate the PE import vector based on hashing values from the import address table (paragraph 0036).
Regarding claim 39, Avasarala et al. teaches wherein the set of PE values includes a PE metadata vector, the instructions further comprising code to cause the one or more processors to: extract a set of numerical PE fields from a representation of the target file; and calculate the PE metadata vector based on the set of numerical PE fields (paragraph 0071).
Regarding claim 40, Avasarala et al. teaches further comprising code to cause the one or more processors to: select a trained threat model based on a type of the target file, the code to cause the one or more processors to calculate the probability includes code to cause the one or more processors to calculate the probability based on the trained threat model (paragraph 0040).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON HOFFMAN whose telephone number is (571)272-3863. The examiner can normally be reached Monday-Friday 8:30AM-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Pwu can be reached at (571)272-6798. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON HOFFMAN/Primary Examiner, Art Unit 2433
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