DETAILED ACTION
-Claims 1, 4, 8, 11, 15, 16 and 18 are amended.
-Objection to claim 15 is withdrawn based on the claim amendments.
-The 112(b) rejection is maintained because the amendment did not resolve the lack of antecedent basis issues in the claims.
-The double patenting rejection is maintained until the final scope of the claims has been determined.
-Claims 1-21 are pending.
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 .
Response to Arguments
Applicant’s Remarks filed on 11/24/2025 have been fully considered however they are moot in view of the new grounds of rejection necessitated by the amendments.
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 1-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11463406. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the issued patent anticipate or render obvious, in view of Higbee and Metsis as applied in the current office action, the claims in the current application.
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 1-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1, 8 and 15 recite on lines 9 and 7, respectively, “the number of matching attributes” which lacks antecedent basis. The dependent claims inherit this rejection.
Claim Rejections - 35 USC § 103
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 1-15 and 17-21 are rejected under 35 U.S.C. 103 as being unpatentable over Higbee et al (US Pub.No.2018/0191754) in view of Metsis et al “Spam Filtering with Naïve Bayes- which Naïve Bayes?”, CEAS Third Conference on Email and Anti-Spam, July 27-28, 2006.
Re Claim 1. Higbee discloses a filtering system, comprising: a processor; a non-transitory computer readable medium, comprising instruction for: obtaining a first identifier extracted from first electronic data; obtaining a first set of attributes extracted from the first electronic data; determining a number of matching identifiers for the first identifier from a set of identifiers by searching a database of identifiers; determining whether the number of matching identifiers exceeds a first threshold (i.e. The rules module can also develop rules based upon reported files and extracted information from the reported messages. ……….. As a message meets specific reporting thresholds, the rules module can be automatically implemented ………….This can include extraction of header information, content information or any other information that the management console module is capable of extracting. The extraction can be automatic upon meeting a specific threshold, such as number of people reporting the same message ……………………..The system can then aggregate the similar characteristics or pattern matching to develop rules. These can include if specific headers are identified, attachments, links, message content or any other element that malware and virus scanning programs detect) [Higbee, para.0103, Note: a specific message identified with msgid1 exceeding a reporting threshold in a reported messages database implies that the number of messages with the same message identifier msgid1 and therefore the number of matching identifiers exceeds a threshold]; in response to the number of matching identifiers exceeding the first threshold, determining a matching attributes specificity for the number of matching attributes based on the first set of attributes (i.e. Messages can be clustered based on the application of rules to messages that have been reported as suspicious. As non-limiting examples, similarities for grouping purposes could be based on parameters such as message attachment name, time, hash of the attachment, a fuzzy hash, or any combination of parameters. ……………………………….. Clusters can be defined based on certain parameters, and then messages matching those parameters can be grouped into those clusters…………………... one such cluster operation may be based on the average distance of the incoming message to all messages in each cluster, wherein a message may be assigned to at least one cluster if the average distance is below some threshold) [Higbee, para.0136-0138, Note: the clustering is performed in response to the average distance of the messages being less than a threshold which implies that it is performed in response to the number of matching identifiers being exceeding the first threshold]; generating a filter (i.e. When a recipe is created from a cluster, the system will automatically associate all the rules common to the cluster with the recipe) [Higbee, para.0110, Note: the recipes are filters],
Higbee does not explicitly disclose whereas Metsis does: generating a filter operable to determine that the matching attributes specificity is less than or equal to a second threshold and applying the filter to second electronic data to reject or accept second electronic data (i.e. we do not assign attributes to tokens that are too rare (we discard tokens that do not occur in at least 5 messages of the training data) ………………………Let us denote by F = {t1, . . . , tm} the set of tokens that correspond to the m attributes after attribute selection. The multi-variate Bernoulli NB treats each message d as a set of tokens, containing (only once) each ti that occurs in d. Hence, d can be represented by a binary vector ~x = (x1, . . . , xm), where each xi shows whether or not ti occurs in d. Furthermore, each message d of category c is seen as the result of m Bernoulli trials, where at each trial we decide whether or not ti will occur in d. The probability of a positive outcome at trial i (ti occurs in d) is p(ti |c). The multi-variate Bernoulli NB makes the additional assumption that the outcomes of the trials are independent given the category. This is a “naive” assumption, since word co-occurrences in a category are not independent. Similar assumptions are made in all NB versions, and although in most cases they are over-simplistic, they still lead to very good performance in many classification tasks; Then, p(~x |c) can be computed as:
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) [Metsis, page 2, Sections 2 -2.1, Note: the classification criterion/filter of Metsis is of the form A/B > T which is the same as B/A ≤ 1/T therefore teaches specificity ≤ a second threshold 1/T];
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify Higbee with Metsis because Naive Bayes (nb) classifiers currently appear to be particularly popular in commercial and open-source spam filters. This is probably due to their simplicity, which makes them easy to implement, their linear computational complexity, and their accuracy [Metsis, page 1, Section 1 Introduction].
Re Claims 8 and 15. These claims recite features similar to those in claim 1 and therefore they are rejected in a similar manner.
Re Claims 2 and 9. Higbee in view of Metsis discloses the features of claims 1 and 8, Higbee in view of Metsis further discloses: wherein applying the filter to second electronic data comprises determining a rejection probability associated with the second electronic data (i.e. the other integrations 1840 may characterize a reported email as “good” or “bad”, i.e. determine with some probabilistic determination whether the reported email is generally malicious or non-malicious to aid an administrator in responding to threats. Alternatively, the characterization of a message as “good” or “bad” may cause the system to automatically perform some action on the message (e.g., quarantine),) [Higbee, para.0164], [Metsis as in claim 1, discloses “second” electronic data].
The same motivation to modify with Metsis, as in claim 1, applies.
Re Claims 3, 10 and 17. Higbee in view of Metsis discloses the features of claims 1, 8 and 15, Higbee further discloses: wherein the set of identifiers were extracted from received electronic data (i.e. The rules module can also develop rules based upon reported files and extracted information from the reported messages. ………..) [Higbee, para.0103].
Re Claims 4, 11 and 18. Higbee in view of Metsis discloses the features of claims 1, 8 and 15, Higbee further discloses: wherein the matching attributes specificity of each of the set of attributes is determined based on matching attributes from multiple electronic data (i.e. FIG. 9 shows a cluster module 900 that is capable of performing a cluster operation on incoming messages 910. The cluster module may perform a cluster operation to group similar messages, as described above. For example, one such cluster operation may be based on the average distance of the incoming message to all messages in each cluster, wherein a message may be assigned to at least one cluster if the average distance is below some threshold…………the distance between two messages is the number of dissimilar rules between them. Here, two emails are “closer” together by having similar rules associated with each other) [Higbee, para.0138, Fig. 9].
Re Claims 5, 12 and 19. Higbee in view of Metsis discloses the features of claims 1, 8 and 15, Metsis further discloses: wherein the first electronic data is the same as the second electronic data (i.e. Split the sequence of messages into batches b1, . . ., bl of k adjacent messages each, preserving the order of arrival…………………For i = 1 to l − 1, train the filter (including attribute selection) on the messages of batches 1, . . . , i, and test it on the messages of bi+1 ) [Metsis, Section 3, page 5. Col.2, all batches include email messages].
The same motivation to modify with Metsis, as in claim 1, applies.
Re Claims 6, 13 and 20. Higbee in view of Metsis discloses the features of claims 1, 8 and 15, Metsis further discloses: wherein the second electronic data is an email message (i.e. Split the sequence of messages into batches b1, . . . , bl of k adjacent messages each, preserving the order of arrival…………………For i = 1 to l − 1, train the filter (including attribute selection) on the messages of batches 1, . . . , i, and test it on the messages of bi+1 ) [Metsis, Section 3, page 5. Col.2, all batches include email messages].
The same motivation to modify with Metsis as in claim 1, applies.
Re Claims 7, 14 and 21. Higbee in view of Metsis discloses the features of claims 1, 8 and 15, Higbee further discloses: wherein the first identifier is an HTML pattern, a link, a domain, or a phone number (i.e. the rule content 1430 may include the sender email address and subject as strings to be used as a match condition………………………. Some rules created may be content-specific, such as rules that match addressee names or domain names) [Higbee, para.0100-0102, Fig.14].
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Higbee et al (US Pub.No.2018/0191754) in view of Metsis et al “Spam Filtering with Naïve Bayes- which Naïve Bayes?”, CEAS Third Conference on Email and Anti-Spam, July 27-28, 2006 and further in view of Bobotek (US Pub.No.2012/0030293).
Re Claim 16. Higbee in view of Metsis discloses the features of claim 15, Higbee in view of Metsis further discloses: wherein applying the filter to second electronic data comprises determining a rejection probability associated with the second electronic data (i.e. the other integrations 1840 may characterize a reported email as “good” or “bad”, i.e. determine with some probabilistic determination whether the reported email is generally malicious or non-malicious to aid an administrator in responding to threats. Alternatively, the characterization of a message as “good” or “bad” may cause the system to automatically perform some action on the message (e.g., quarantine),) [Higbee, para.0164], [Metsis as in claim 1, discloses “second” electronic data].
The same motivation to modify with Metsis, as in claim 1, applies.
Higbee in view of Metsis does not explicitly disclose whereas Bobotek discloses: and based on an accepted attribute count relative to a total accepted count (i.e. The MADC 116 can apply a predefined content correlation metric to the respective content correlation results of the evaluated hash values to obtain a spam content correlation result (e.g., to quantify the correlation of the mobile message content to known spam) to facilitate classifying the mobile message, in accordance with the predefined message abuse criteria. The predefined content correlation metric can be desirably structured or defined such that proper weighting or scoring is given to respective results for respective hash values (e.g., a result that indicates spam can be given more weight than a result that indicates "ham")) [Bobotek, para.0120].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify Higbee in view of Metsis with Bobotek so that a spammer is not able to disguise a spam mobile message as a "ham" mobile message by also including a significant amount of "ham" content in the spam mobile message (e.g., inserting a significant amount of "ham" content amongst spam content in a mobile message) in order to hinder detection of the spam mobile message [Bobotek, para.0120].
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.
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/NOURA ZOUBAIR/Primary Examiner, Art Unit 2434