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. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(d): (d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers. The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph: Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers. Claims 6 and 15 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Specifically, claims 6 and 15 each provides that the training “utilizes an unsupervised learning technique and/or a supervised learning technique.” As these are presented in the alternative, only one of the t hree options (supervised, unsupervised, or a combination of supervised and unsupervised) are required to teach the instant claim, as a whole . This appears to cover every possible option for machine learning technique, and thus fails to actually limit the training (which would require some machine learning technique) of claims 1 and 10. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements. 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. Claim (s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2015/0319072 (Chakrabarti) in view of US 10,524,141 ( Guven ) . With regard to claim 1, Chakrabarti discloses method, the method comprising: at a test system: performing, using test configuration information, a plurality of fuzz testing sessions involving one or more systems under test (SUT), wherein at least some of the plurality of fuzz testing sessions include different test traffic parameters and/or SUT configurations than at least one of the plurality of fuzz testing sessions ( Chakrabarti : Abstract, Paragraphs [0029] and [0047, and Figures 7-9. Chakrabarti discloses fuzz testing, where at least test traffic can be provided for different testing sessions.); obtaining fuzz test data from one or more sources, wherein the fuzz test data includes test traffic data and SUT performance data associated with the plurality of fuzz testing sessions ( Chakrabarti : Paragraph [0103] and Figure 6.); Chakrabarti fails to disclose, but Guven teaches that the method is for training a machine learning model for indicating a stress state value associated with a system under test (SUT); training, using the fuzz test data and one or more machine learning algorithms, a machine learning model for receiving as input traffic data associated with test traffic or live traffic involving a respective SUT and SUT performance data associated with the test traffic or live traffic and providing as output a stress state value indicating the likelihood of the respective SUT crashing or failing; and storing, in a machine learning model data store, the trained machine learning model for subsequent use by the test system or a SUT analyzer ( Guven : Column 11 , line 34 to Column 12, line 13. Guven provides for the training of machine learning models based on previous data of the nodes, where the model can use inputs to predict a likelihood of node failure. When this teaching is applied to fuzz testing, which serves to provide many different conditions to determine possible failures ( Chakrabarti : Paragraph [0026]), such tests would serve as the previous data to train the models, with further input enabling additional inputs, whether test or live inputs, and provide a likelihood of node failure.). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Chakrabarti to utilize the fuzz test data of Chakrabarti as training data for a machine learning model, such as in Guven , to improve the effectiveness of such fuzz testing, allowing the testing to be used to predict possible system failures beyond the limitations of the actual fuzz testing . With regard to claim 2, Chakrabarti in view of Guven fails to teach, but knowledge possessed by one of ordinary skill in the art at the time of filing teaches wherein the one or more machine learning algorithms includes an artificial neural network, a feedforward neural network, a recurrent neural network, or a convolutional neural network (More specifically, Official Notice is taken that each of these listed types of machine learning algorithms were well-known to one of ordinary skill in the art.). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to utilize at least one of these well-known types of algorithms to realize the well-known benefits of such. For instance, Artificial Neural Networks were known to efficiently learn patterns from data and improve performance over time, with at least this type of algorithm working well with learning patterns from the fuzz testing and making predictions beyond the actual tests. With regard to claim 3, Chakrabarti discloses wherein the fuzz test data used in training includes test results, wherein the test results include a final binary result indicating pass or failure for a respective SUT at the end of a respective fuzz testing session; and a final operator-provided value or metric for a respective SUT at the end of a respective fuzz testing session ( Chakrabarti : Paragraphs [0024] and [0067]. The fuzz testing indicates failures of the device under test . ). Chakrabarti fails to teach, but knowledge possessed by one of ordinary skill in the art at the time of filing teaches that the value or metric is on a predetermined scale indicating a likelihood or nearness to failure (More specifically, Official Notice is taken that the providing of expected output by a human operator for training machine learning models was well-known to one of ordinary skill in the art at the time of filing, where Guven teaches the output of a likelihood of node failure ( Guven : Column 11, lines 34 to 62).). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to have a human operator, such as that of Chakrabarti , provide a likelihood of failure in a manner that would be expected for an output of the system (predetermined scale) to improve the training of the machine learning model to ensure that examples are provided of the actual expected output for at least some of the tests. With regard to claim 4, the instant claim is within the scope of claim 3, and is rejected for similar reasons. With regard to claim 5, Chakrabarti teaches wherein the fuzz test data used in training is correlated using timestamps ( Chakrabarti : Paragraph [0025]) . With regard to claim 6, Chakrabarti in view of Guven teaches the training of the machine learning model utilizes an unsupervised learning technique and/or a supervised learning technique ( Guven : Column 11, line 34 to Column 12, line 13. The providing of the test data would utilize at least one of a supervised and unsupervised technique.) . With regard to claim 7, Chakrabarti in view of Guven teaches at the SUT analyzer: receiving, via the test system or the machine learning model data store, the trained machine learning model; receiving traffic data and SUT performance data associated with network traffic involving a first SUT; generating, using the traffic data and the SUT performance data as input to the trained machine learning model, a stress state value associated with the first SUT; and providing the stress state value to a display, a user, or another entity (Chakrabarti: Paragraph [0035] and Guven : Column 11, line 34 to Column 12, line 13 . In Chakrabarti, the system may be controllable by a human operator, such that the human is presented different GUIs for controlling the tests and/or reviewing the results. Meanwhile, in Guven , the machine learning model, trained based on previous data, can be used for making predications based on additional data, which when such is under control of a human operator, would present those results to the human as well.). With regard to claim 8, Chakrabarti in view of Guven teaches at the test system: receiving traffic data and SUT performance data associated with an on-going or completed first fuzz testing session involving a first SUT; generating, using the traffic data and the SUT performance data as input to the trained machine learning model, a stress state value associated with the first SUT; and providing the stress state value to a display, a user, or another entity (Chakrabarti: Paragraph [0035] and Guven : Column 11, line 34 to Column 12, line 13. In Chakrabarti, the system may be controllable by a human operator, such that the human is presented different GUIs for controlling the tests and/or reviewing the results. Meanwhile, in Guven , the machine learning model, trained based on previous data, can be used for making predications based on additional data, which when such is under control of a human operator, would present those results to the human as well.) . With regard to claim 9, Chakrabarti in view of Guven teaches wherein the traffic data includes copies of network traffic, log data, or traffic metrics and at least some of the traffic data is obtained from the test system, a fuzz testing module, a traffic generator, a monitoring agent, a network probe, a network tap, one or more data repositories, or the SUT (Chakrabarti: Figure 6. The at least the fuzz testing module would provide traffic metrics (data about the traffic).) ; and wherein the SUT performance data includes performance or health statistics or metrics, SUT state information, error information, or failure information and at least some of the SUT performance data is obtained from the test system, the one or more data repositories, or the SUT (Chakrabarti: Paragraph [0044]. At least failure information would be received from, for example, the test system.) . With regard to claims 10-18, the instant claims are similar to claims 1-9, and are rejected for similar reasons. With regard to claims 19-20, the instant claims are similar to claims 1-2, and are rejected for similar reasons. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT SCOTT B CHRISTENSEN whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1144 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday through Friday, 6AM to 2PM . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT John Follansbee can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-3964 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. FILLIN "Examiner Stamp" \* MERGEFORMAT SCOTT B. CHRISTENSEN Examiner Art Unit 2444 /SCOTT B CHRISTENSEN/ Primary Examiner, Art Unit 2444