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 .
1. Claims 2, 4, 6-8, 10, 12-14, 16 and 18-19 have been amended. Claims 1-19 have been examined.
Response to Arguments
2. Applicant’s arguments, see pages 6-7, filed 07/17/2026, with respect to the 35 USC 101 analysis of amended claim, 8 and 14 have been fully considered and are persuasive. The 35 USC 101 rejection of 05/07/2026 has been withdrawn.
Applicant’s arguments with respect to the 35 USC 102 rejection of claims 2, 8 and 14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
3. Claims 2-19 are objected to because of the following informalities:
Claims 2, 8 and 14 recite, “obtaining output characteristics of a first machine learning model executed in the network”. However, due to the claim amendments, “the network” no longer has antecedent basis.
Appropriate correction is required.
4. 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.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
5. Claims 2-4, 6-10, 12-16 and 18-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chen et al. (U.S. Patent Application Publication 2017/0024660; hereafter “Chen”).
For claims 2, 8 and 14, Chen teaches a method, system and non-volatile computer-readable media (note paragraph [0157], memory) for dynamically detecting and responding to deviations in machine learning model quality, the system comprising:
one or more devices each including a processor and a memory, wherein the one or more devices are operable to execute instructions which cause the system to perform operations (note paragraphs [0058] and [0155]-[0157], smartphones and servers with memory storing instructions for execution by a processor) including:
obtaining output characteristics of a first machine learning model executed in the network (note Fig. 3 and paragraphs [0105] and [0112]-[0114], network server receives information on conditions, features, behaviors and corrective actions from the cloud service which were feedback back Behavioral Analysis Module in client computers; default classifier model is set as current classifier model and known behavior vectors are classified);
determining a measure of accuracy of the first machine learning model based on the output characteristics of the first machine learning model (note paragraphs [0114]-[0115], classifier accuracy of current classifier model is determined);
determining, based on the measure of accuracy of the first machine learning model that the first machine learning model should be replaced (note paragraph [0115], determination whether refined classifier model exceeds a threshold, i.e. refined classifier model should replace previous classifier model);
in response to determining that the first machine learning model should be replaced, automatically deploying, for execution, a second machine learning model to the second device in place of the first machine learning model (note paragraph [0116], in response to accuracy threshold is exceeded, processor may send the refined classifier model to a client computing device to classify behavior, i.e. automatically deploying for execution), wherein the second machine learning model has a higher measure of accuracy than the first machine learning model (note paragraph [0115], refinement operations are repeated until accuracy threshold is exceeded, i.e. refined model sent to client computing device has a higher accuracy value than the previous classifier model); and
executing the second machine learning model after its deployment (note paragraphs [0107] and [0111], mobile device executes classifier model in its behavior-based security system to analyze device behaviors).
For claims 3, 9 and 15, Chen teaches claims 2, 8 and 14, wherein the machine learning model is designed to detect network traffic anomalies (note paragraphs [0034], [0064]-[0065], [0068] and [0073], model detects anomalous behavior; behaviors detected include the network traffic of the client device).
For claims 4, 10 and 16, Chen teaches claims 2, 8 and 14, wherein the output characteristics are obtained by a supervisory and control agent (SCA) (note paragraphs [0045] and [0105], network server receives behavior data from a central location and uses it to generate a full classifier model, i.e. supervisory and control agent).
For claims 6, 12 and 18, Chen teaches claims 2, 8 and 14, the operations further including training the second machine learning model prior to substituting the first machine learning model with the second machine learning model (note paragraphs [0114]-[0116], refined model is trained prior to deployment to client devices).
For claims 7, 13 and 19, Chen teaches claims 2, 8 and 14, the operations further including registering the second machine learning model for distribution to a second device in the network other than a supervisory and control agent (SCA) (note paragraph [0114], new classifier model is set as the current model before sending to client devices, i.e. registered, for deployment to endpoint devices).
6. Claims 2, 8 and 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ando et al. (U.S. Patent Application Publication 2015/0379432; hereafter “Ando”).
For claims 2, 8 and 14, Ando teaches a method, system and non-volatile computer-readable media (note paragraphs [0096]-[0098], memory) for dynamically detecting and responding to deviations in machine learning model quality, the system comprising:
one or more devices each including a processor and a memory, wherein the one or more devices are operable to execute instructions which cause the system to perform operations (note paragraphs [0093] and [0098], model updating device with memory storing instructions for execution by a processor) including:
obtaining output characteristics of a first machine learning model executed in the network (note paragraphs [0060]-[0061] and [0108]-[0113], model prediction results are obtained and judged);
determining a measure of accuracy of the first machine learning model based on the output characteristics of the first machine learning model (note paragraphs [0062] and [0116], model accuracy rate is determined);
determining, based on the measure of accuracy of the first machine learning model that the first machine learning model should be replaced (note paragraphs [0063] and [0117], model update judging unit judges if it necessary to update the model based on the accuracy rate);
in response to determining that the first machine learning model should be replaced, automatically deploying (note paragraphs [0105] and [0120], model update is performed by a control program in a computer, i.e. automatically), for execution, a second machine learning model to the second device in place of the first machine learning model (note paragraphs [0117]-[0118] and [0126], in response to determination update is necessary, model update is performed and new model is adopted, i.e. deployed), wherein the second machine learning model has a higher measure of accuracy than the first machine learning model (note paragraphs [0124] and [0126], updated model with highest accuracy that exceeds the model before is adopted); and
executing the second machine learning model after its deployment (note paragraphs [0048], [0060] and [0064], currently deployed model is executed to make predictions).
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.
7. Claims 5, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chen as applied to claims 2, 8 and 14 above, and further in view of DiCorpo et al. (U.S. Patent Application Publication 2012/0150773; hereafter “DiCorpo”).
For claims 5, 11 and 17, Chen differs from the claimed invention in that they fail to teach:
the operations further including displaying a visual comparison of the change in model quality to a user
DiCorpo teaches:
the operations further including displaying a visual comparison of the change in model quality to a user (note paragraphs [0057] and [0070], user interface displays a change report including differences in quality metrics between previous and modified MLD profiles).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the recursive refinement of behavior classifying models of Chen the user interface of DiCorpo. One of ordinary skill would have been motivated to combine Chen and DiCorpo because a user interface would be a convenient way for an administrator to monitor the refinements made to the behavior model through the iterations.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Agarwal et al. (U.S. Patent Application Publication 2017/0222960) discloses comparing the performance score of a candidate model with the current active model before automatic deployment (note paragraph [0052]).
9. 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.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID J PEARSON whose telephone number is (571)272-0711. The examiner can normally be reached 8:30 - 6:00 pm; Monday through Friday.
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DAVID J. PEARSON
Primary Examiner
Art Unit 2407
/David J Pearson/Primary Examiner, Art Unit 2407
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