DETAILED ACTION
This office action is in response to the correspondence filed on 12/03/2025. This application is a continuation of 17332174 filed 05/27/2021 that has a foreign application RU2020120449 filed 06/19/2020. Claims 1-21 are pending and are examined.
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 .
Priority
Applicant's claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Arguments
The amendments and/or arguments submitted by Applicants for the objection(s)/rejection(s) listed below have been considered and are persuasive; thus, they have been withdrawn:
Claim Objection(s)
Regarding the non-statutory double patenting rejection, the rejection is maintained since there is no change of claim scope.
Applicant’s arguments with respect to claims 1, 8, and 15 have been considered but are moot because the arguments do not apply to the new combination of the references being used in the current rejection. The new reference(s) was/were necessitated by the amendment filed by the applicant. The rejection is presented below.
The newly amended claims are rejected below in view of Scherman et al. (US Pub No. 2019/0266325 A1, referred to as Scherman).
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 §§ 706.02(l)(1) - 706.02(l)(3) 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11997494 B2 (US Application No. 17332174). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the instant application are anticipated by the patented claims. The claims in the instant application are essentially the same while slightly broader in scope than the ones in the issued patent. The instant application has the basic elements of intercepting an event by a mobile device; analyzing the event; and classifying the event as undesirable if a rating value of similar events is less than a predetermined threshold value, as detailed in both claims while the issued patent has the additional of a mobile user profile comprises a vector containing elements of a matrix of user preferences as seen in the example below in claim 1 of the instant application and claim 1 of the issued patent.
Examiner notes that the “to determine…” in the amended claim is intended use language and holds no patentable weight. Therefore, it does not affect the double patenting determination. Applicant should change it to positively recite the feature. In addition, this additional feature, even when positively recited, is taught by a new prior art Scherman. Therefore, it would still be rejected under an obvious double patenting rejection.
Instant Application
U.S. Patent No. 11997494 B2
1. A method for classifying incoming events by user’s mobile device based on user preferences, the method comprising:
intercepting an incoming event received by a mobile device;
analyzing content of the intercepted event to determine one or more attributes associated with words in the content of the intercepted event;
comparing the intercepted event to a plurality of previously collected and classified events, stored in an event repository, based on the one or more determined attributes to identify one or more similar events;
determining a rating value of the one or more similar events based on a matrix of user preferences, wherein the rating value indicates probability that the one or more similar event belongs to a particular class of events representing events of user interest; and
classifying the intercepted event as undesirable on the mobile device if the rating value of the one or more similar events is less than a predetermined threshold value.
1. A method for classifying incoming events by user's mobile device, the method comprising:
intercepting an incoming event received by a mobile device;
analyzing content of the intercepted event to determine one or more attributes of the intercepted event;
comparing the intercepted event to a plurality of previously collected and classified events, stored in an event repository, based on the one or more determined attributes to identify one or more similar events;
determining a rating value of the one or more similar events, wherein the rating value indicates probability that the corresponding event belongs to a particular class of events; and
classifying the intercepted event as undesirable on the mobile device if the rating value of the one or more similar events is less than a predetermined threshold value, wherein classifying the intercepted event comprises determining a mobile user profile from the plurality of previously collected and classified events, wherein the mobile user profile comprises a vector containing elements of a matrix of user preferences.
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 1-3, 8-10 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Spievak et al. (US Pub No. 2015/0249737 A1, referred to as Spievak), in view of Scherman et al. (US Pub No. 2019/0266325 A1, referred to as Scherman).
Regarding claims 1, 8, and 15, taking claim 8 as exemplary, Spievak discloses,
8. A system for classifying incoming events by user’s mobile device based on user preferences, the system comprising:
at least one memory; and (Spievak: Fig. 2)
at least one hardware processor coupled with the at least one memory and configured, individually or in combination, to: (Spievak: Fig. 2)
intercept an incoming event received by a mobile device; analyze content of the intercepted event to determine one or more attributes …of the intercepted event; (Spievak: Fig. 2; [0010]; a first call to the first phone address is received. Prior to answering the first call and at least partly in response to a determination that a calling party phone address of the first call matches one of the previously stored calling party phone address, the first call is identified as a spam or otherwise undesirable call candidate and may be rejected (intercept the call event after the call is received but before it’s answered) [0010]; a determination that a calling party phone address (attribute) of the first call matches one of the previously stored calling party phone address. [0030] analyzing the calling party phone address.)
compare the intercepted event to a plurality of previously collected and classified events, stored in an event repository, based on the one or more determined attributes to identify one or more similar events; (Spievak: [0010]; to a determination that a calling party phone address (attribute) of the first call matches (compare) one of the previously stored calling party phone address (previously collected), the first call is identified as a spam or otherwise undesirable call candidate and may be rejected (identify similar events by matching). [0011]; a stored call block indication (classified as block indication).)
determine a rating value of the one or more similar events based on a matrix of user preferences, wherein the rating value indicates probability that the one or more similar event belongs to a particular class of events representing events of user interest; and (Spievak: [0029]; examples of call data which can be converted into a vector of latent scores (rating values) include but are not limited to: detected dtmf tone combinations, detected dtmf tone cadence, detection of certain audio frequencies, etc. Neighborhood models are based on the idea that "things", be they users or items, in a system might be "like" other "things" in the system (similar events that the users might or might not be interested in). Based on the scores assigned to those other "things" by the user (a matrix of user preferences), derive a predicted score for the item in question (probability that the event belongs to a certain neighborhood of similar calls/class of events). In certain embodiments the neighborhood of similar calls is utilized. Optionally, the system determines via a mechanism, for each call, what other calls are in its neighborhood. This mechanism is referred to as a distance measurement.)
classify the intercepted event as undesirable on the mobile device if the rating value of the one or more similar events is less than a predetermined threshold value. (Spievak: [0028]; a threshold value and if the item's predicted score is above the threshold value, the item will be recommended to the user (i.e. undesirable if less than the threshold).)
Spievak does not explicitly disclose, however Scherman teaches,
…analyzing content of the intercepted event to determine one or more attributes associated with words in the content of the intercepted event; (Scherman: [0014]; a text-based representation of the user session can be generated. For instance, process events can correspond to words and sequences of words can correspond to sentences. The text-based representation of each session can be classified with a text-based classifier as malicious or non-malicious based on the sequence of process events, or words, within a session, or sentence.)
Examiner notes that the “to determine…” is intended use language and holds no patentable weight. Applicant should change it to positively recite the feature.
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Scherman into the teachings of Spievak with a motivation to enable automatic and accurate detection of malicious sessions including artificial neural networks by employing a text-based classifier to classify text representations of sessions as malicious or non-malicious based on the sequence of process events in the text representation (Scherman: [0003]).
Regarding claims 2, 9, and 16 taking claim 9 as exemplary, the combination of Spievak and Scherman discloses,
9. The system of claim 8,
Spievak further discloses,
wherein the rating value is based on one or more rating attributes that comprise at least one of: parameters of a media content of the intercepted event, time duration from the moment of interception of the intercepted event, a flag for viewing the media content of the intercepted event by a user, a flag for removal of the media content of the intercepted event by the user. (Spievak: [0029]; examples of call data (rating value attributes) which can be converted into a vector of latent scores (rating values) include but are not limited to: detected dtmf tone combinations, detected dtmf tone cadence, detection of certain audio frequencies, etc. (parameters of a media content of the call).)
Regarding claims 3, 10, and 17 taking claim 10 as exemplary, the combination of Spievak and Scherman discloses,
10. The system of claim 8,
Spievak does not explicitly disclose, however Scherman teaches,
wherein the one or more determined attributes include at least one of: words, a sequence of words, a vectorial representation of words, or multiple sets of words representing a bag-of-words. (Scherman: [0014]; a text-based representation of the user session can be generated. For instance, process events can correspond to words and sequences of words can correspond to sentences. The text-based representation of each session can be classified with a text-based classifier as malicious or non-malicious based on the sequence of process events, or words, within a session, or sentence.)
The same motivation that was utilized for combining Spievak and Scherman as set forth in claim 8 is equally applicable to claim 10.
Claims 4, 7, 11, 14, 18, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Spievak, in view of Scherman, in view of Seifert et al. (US Pub No. 2021/0141897 A1, referred to as Seifert).
Regarding claims 4, 11, and 18 taking claim 11 as exemplary, the combination of Spievak and Scherman discloses,
11. The system of claim 8,
Spievak further discloses.
…rating value (Spievak: [0029]; examples of call data which can be converted into a vector of latent scores (rating values).)
The combination of Spievak and Scherman does not explicitly disclose, however Seifert teaches,
wherein the …[data] is determined using a machine learning model based on one or more …[data] attributes, and (Seifert: [0047]; training a machine learning model by using the data selected via the training set construction component 207 and/or the other components. [0049]; the unknown evaluator 211 is generally responsible for determining which malicious sets of content (within the labeled data 213) are similar to unknown sets of content (i.e., it is not known whether the unknown sets contain malicious content) (within the unknown data 215) and scoring similarity accordingly.)
wherein the machine learning model comprises one of: a naive Bayesian classifier model, a logistic regression model, a Markov Random Field (MRF) classifier model, a Support Vector Machine (SVM) model, a k nearest-neighbor model, and a decision tree model. (Seifert: [0076]; responsive to the similarity model being trained, the KNN (K-Nearest Neighbor) index building module 414 receives the extracted strings and behavioral features generated by the detonation and extraction module 404 and further receives the labels from the label database 406 and further receives portions of the similarity model 416 to build out a KNN index 412.)
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Seifert into the combination of Spievak and Scherman of incoming event classifying system to include the machine learning and cosine distance features as taught in Seifert with a motivation to apply a known technique of features learning to a known method ready for improvement to yield predictable results of enhanced learning.
Regarding claims 7, 14, and 21 taking claim 14 as exemplary, the combination of Spievak and Scherman discloses,
14. The system of claim 8,
Spievak further discloses.
wherein comparing the intercepted event to the plurality of previously collected and classified events comprises further comprises: (Spievak: [0010])
generating a vector representing the intercepted event; (Spievak: [0029]; examples of call data which can be converted into a vector of latent scores.)
generating a plurality of vectors representing the plurality of previously collected and classified events; and (Spievak: [0029]; examples of call data which can be converted into a vector of latent scores include but are not limited to: detected dtmf tone combinations, detected dtmf tone cadence, detection of certain audio frequencies, etc. Neighborhood models are based on the idea that "things", be they users or items, in a system might be "like" other "things" in the system. Based on the scores assigned to those other "things" by the user (vectors representing previously collected and classified calls/events in the neighborhoods), derive a predicted score for the item in question. In certain embodiments the neighborhood of similar calls is utilized. Optionally, the system determines via a mechanism, for each call, what other calls are in its neighborhood. This mechanism is referred to as a distance measurement.)
calculating a similarity value between the intercepted event and each of the plurality of previously collected and classified events based on …distance values between corresponding vectors. (Spievak: [0029]; based on the scores assigned to those other "things" by the user, derive a predicted score for the item in question (calculate a similarity value that the event belongs to a certain neighborhood of similar calls/class of events). In certain embodiments the neighborhood of similar calls is utilized. Optionally, the system determines via a mechanism, for each call, what other calls are in its neighborhood. This mechanism is referred to as a distance measurement.)
The combination of Spievak and Scherman does not explicitly disclose, however Seifert teaches,
…cosine distance values (Seifert: [0026]; learn an embedding vector based on deep learning to detect similar computer objects or indications in feature space using distance measures, such as cosine distance.)
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Seifert into the combination of Spievak and Scherman of incoming event classifying system to include the machine learning and cosine distance features as taught in Seifert with a motivation to apply a known technique of features learning to a known method ready for improvement to yield predictable results of enhanced learning.
Allowable Subject Matter
Claims 5-6, 12-13, and 19-20 contain allowable subject matter but remain rejected under nonstatutory double patenting rejections. They are also objected to as being dependent upon rejected base claims, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims; and the stated rejection(s) are resolved.
The following is an examiner’s statement of reasons for allowance:
Although prior arts Spievak, Scherman and Seifert above disclose all the limitations of the prior claims (see rejections above), none of the prior arts of record alone or in combination discloses determining a mobile user profile from previously collected and classified events, wherein the mobile user profile comprises a vector containing elements of a matrix of user preferences; reconstruct approximated values of the matrix of user preferences using a gradient descent optimization and wherein the rating value is determined based on zero values of the reconstructed matrix of user preferences as described in the claims.
At the effective filing date of the application, the above limitations would not have been obvious over the prior arts of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
LANGTON J A et al. US-PGPUB US 20170323101 A1 Device for dynamically optimizing performance of security appliance detecting e.g. worm in text object of smartphone, has processors updating threat prediction model based on set of features, set of threat scores and set of utility values
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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KA SHAN CHOY whose telephone number is (571) 272-1569. The examiner can normally be reached on MON - FRI: 9AM-5:30PM EST Alternate Fridays.
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/KA SHAN CHOY/Primary Examiner, Art Unit 2435