DETAILED ACTION
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 .
Status of Claims
Claims 1-20 are pending in this application.
Claim Rejections - 35 USC § 102
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.
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.
Claim(s) 1,4-7,10-12,16-17,20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mann et al(US 2020/0372415).
Claim 1: Mann disclose a processor; and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations in (fig.3;page 6[0076]). Mann disclose based on test log data representative of test logs and using machine learning attention, generating a test log output vector in (pages 1-2[0012-0013]:features includes at least one incident parameter wherein the monitoring data includes machine generated textual data; applying a machine learning model is configured to output a suitable insight for an incident represented by the at least one incident parameter wherein the suitable insight is selected from among a plurality of historical insights and generating a predictive ticket based on the suitable insight). Mann disclose based on the test log output vector and a service ticket output vector, determining a probability of a relation between a test log represented in the test log output vector and a service ticket represented in the service ticket output vector; and in response to the probability being determined to satisfy a threshold relation probability, marking the test log and the service ticket as related in (page 2[0029-0030]; page 4[0043]:The historical data set includes machine generated textual data, where anomalies in the machine generated textual data may be indicative that an incident has occurred or will occur. A predictive ticket may be created based on the suitable insight. A suitable insight is selected for an incident based on a suitable score output by a suitability model using incident parameters of the incident and insight parameters of insights as inputs. The machine generated textual data generated by the sources may be application logs, configuration files, audit records etc.).
Claim 2: Mann disclose generating the test log output vector using a test log neural network applied to the test log data in (page 1[0012]).
Claim 4: Mann disclose he service ticket output vector is generated using a service ticket neural network applied to service ticket data representative of service tickets in (page 2[0014]).
Claim 5: Mann disclose the service ticket data comprises a service ticket sparse matrix in (page 4[0044];page 10[0128]).
Claim 6: Mann disclose service ticket neural network comprises a rectified linear unit activation function in (page 2[0013], [0028]).
Claim 7: Mann disclose test log data comprises a test log sparse matrix in (page 4[0044]; page 10[0128]).
Claim 10: Mann disclose based on test log data representative of test logs and using machine learning attention, generating a test log output vector in (pages 1-2[0012-0013]:features includes at least one incident parameter wherein the monitoring data includes machine generated textual data; applying a machine learning model is configured to output a suitable insight for an incident represented by the at least one incident parameter wherein the suitable insight is selected from among a plurality of historical insights and generating a predictive ticket based on the suitable insight). Mann disclose based on the test log output vector and a service ticket output vector, determining a probability of a relation between a test log represented in the test log output vector and a service ticket represented in the service ticket output vector; and in response to the probability being determined not to satisfy a threshold relation probability, determining that the test log comprises a previously unidentified problem in (page 2[0029-0030]; page 4[0043]:The historical data set includes machine generated textual data, where anomalies in the machine generated textual data may be indicative that an incident has occurred or will occur. A predictive ticket may be created based on the suitable insight. A suitable insight is selected for an incident based on a suitable score output by a suitability model using incident parameters of the incident and insight parameters of insights as inputs. The machine generated textual data generated by the sources may be application logs, configuration files, audit records etc.).
Claim 11: Mann disclose generating the test log output vector using a test log neural network applied to the test log data in (page 1[0012]).
Claim 12: Mann disclose the test log neural network comprises a rectified linear unit activation function in (page 2[0013], [0028]).
Claim 16: Mann disclose based on test log data representative of test logs and using machine learning attention, generating, by a system comprising a processor, a test log output vector; and based on the test log output vector and a service ticket output vector, determining, by the system, a probability that a test log represented in the test log output vector is related to a service ticket represented in the service ticket output vector in (page 2[0029-0030]; page 4[0043]:The historical data set includes machine generated textual data, where anomalies in the machine generated textual data may be indicative that an incident has occurred or will occur. A predictive ticket may be created based on the suitable insight. A suitable insight is selected for an incident based on a suitable score output by a suitability model using incident parameters of the incident and insight parameters of insights as inputs. The machine generated textual data generated by the sources may be application logs, configuration files, audit records etc.).
Claim 17: Mann disclose generating, by the system, an output representative of the probability in (fig.11).
Claim 20: Mann disclose the test log data comprises a test log sparse matrix in (page 4[0044];page 10[0128]).
Allowable Subject Matter
Claims 2-3,8-9,13-15,18-19 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
USPTO Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HOSUK SONG whose telephone number is (571)272-3857. The examiner can normally be reached Mon-Fri: 7:30AM-5:00PM.
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, Amir Mehrmanesh can be reached at 571-270-3351. 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.
/HOSUK SONG/Primary Examiner, Art Unit 2435