DETAILED ACTION
This action is in response to the filing 06/29/2023. Claim 1-20 are pending and have been fully 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 .
Status of the Claims
Claims 1-3, 5-10, 15-17, and 20 are rejected under 35 U.S.C. 103.
Claims 4, 11-14, and 18-19 contain allowable subject matter but are objected to as being dependent upon a rejected base claim.
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, 5-10, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Naik et al. (U.S. PGPub No. 20250285749) in view of Brown et al. (U.S. PGPub No. 20210141900).
Regarding Claim 1, Naik teaches,
A computer implemented method for identifying a root cause of a problem, the computer implemented method comprising: using, by a number of processor units, a machine learning model to predict a set of initial log files for review to identify the root cause of the problem (a method [0029]; a computer system comprising a number of processors [0037]; a computer-readable storage medium having computer-readable program code embodied therewith [0036]; at step (130), a neural network (machine learning model) is used to predict a probability that a particular log file will assist in resolution of the fault [Fig. 1; 0073]);
displaying, by the number of processor units, the set of initial log files predicted by the machine learning model … (where the method 100 comprising step (130) identifies log files for resolution to a fault, the log file to be identified is a suggestion of the next log file for a user to review [0068]; where the user's browsing data of the (first) file is further provided to the NN to produce the second prediction [0083-0084]; therefore, the "initial log files" are displayed);
predicting, by the number of processor units, a set of next log files for review to identify the root cause of the problem using the machine learning model and user behavior data related to the graphical user interface in response to a user input to the graphical user interface (a second prediction result is generated by the machine learning algorithm, where the second recommendation is based on the problem data and updated log file browsing data [0083-0084]; problem data may be received by a respondent (end user or service engineer [0022])via an input interface [0071]; the browsing data comprises usage history of a log file viewer application [0021] and describes one or more log files already viewed by the user to resolve the fault [0069]);
and displaying, by the number of processor units, a recommendation to review the set of next log files predicted by the machine learning model … (upon a second log file for resolution of the fault being identified, a suggestion is determined such that a browsing path is suggested to the user [0085-0086]).
While Naik discloses that log files are provided to the user for review [0068], Naik does not appear to disclose and Brown teaches,
Displaying… on a graphical user interface (a graphical user interface may be used to display logs [0352])
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of predicting log files and presenting them to a user for review as disclosed by Naik to include the use of a graphical user interface as disclosed by Brown. The resulting combination improves ease of use by the user by providing an interface for the user to review the log files.
Regarding Claim 2, Naik teaches,
The computer implemented method of claim 1, wherein at least one of an input field for a search query, a search result, keywords highlighted from a search of a log file, a set of related customer cases, or a problem description for the problem are displayed on the graphical user interface (problem data may be provided by a respondent in response to a fault analysis questionnaire ("input field for … a search query ... a problem description") [0022]).
Regarding Claim 3, Naik teaches,
The computer implemented method of claim 1 further comprising: training, by the number of processor units, the machine learning model to predict the set of next log files (the machine learning algorithm is trained to output a prediction for a log file when provided with browsing data and a description of the fault [0019]),
wherein a training dataset comprising sequences of log file names used in determining the root cause of the problem and problem descriptions for the problem is used to train the machine learning model (the training data includes problem data and log file browsing data used to provide assistance in resolution of the fault [0019]; in considering browsing data, the machine learning model considers previous routes to solutions learnt ("sequences") [0087]).
Regarding Claim 5, Naik teaches,
The computer implemented method of claim 4 further comprising: retraining, by the number of processor units, the machine learning model using reinforcement learning and a training dataset comprising user behavior data collected from user behavior … (subsequent log file predictions may be leveraged as a feedback loop for reinforcement learning to improve subsequent predictions [0092]; log file predictions consider browsing data ("user behavior data") [0078, 0083]).
While Naik discloses that log files are provided to the user for review [0068], Naik does not appear to disclose and Brown teaches,
… with respect to the graphical user interface ((a graphical user interface may be used to display logs [0352]; the GUI additionally includes functionality to receive user input [0349]))
The same motivation for Claim 1 also applies to Claim 5.
Regarding Claim 6, Naik teaches,
The computer implemented method of claim 1, wherein the user behavior data is selected from at least one of explicit user data, implicit user data, sequences of log files viewed by a user, a selection of a log file for review, a search query, a set of keywords displayed on the graphical user interface, a portion of the log file reviewed, or an amount of time the log file was reviewed (browsing data describes routes/paths of log files viewed ("sequences of log files viewed by a user") and is updated with each file reviewed by the user [0083, 0087]; log file browsing data may be considered a browsing trail of a particular service engineer ("implicit/explicit user data") [0094]; browsing data may include a portion of the log file viewed [0095]).
Regarding Claim 7, Naik teaches,
The computer implemented method of claim 1, wherein the machine learning model is selected from a group comprising a transformer machine learning model, a Bidirectional Encoder Representations from Transformers (BERT) machine learning model, a neural network work, and a recurrent neural network (the problem data is provided to a neural network [0073]; the neural network may also be a recurrent neural network [0075]).
Claims 8-10 recite a shift in statutory category and are rejected by the same grounds presented for Claims 1-3 as being unpatentable over Naik in view of Brown.
Claims 15-17 and 20 recite a shift in statutory category and are rejected by the same grounds presented for Claims 1-3 and 6, respectively, as being unpatentable over Naik in view of Brown.
Allowable Subject Matter
Claims 4, 11-14, and 18-19 are 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. The following is the Examiner’s statement of reasons for indicating allowable subject matter:
Regarding Claims 4, 11, and 18, Naik teaches the training data includes problem data and log file browsing data used to provide assistance in resolution of the fault [0019]; in considering browsing data, the machine learning model considers previous routes to solutions learnt ("sequences") [0087] subsequent log file predictions may be leveraged as a feedback loop for reinforcement learning to improve subsequent predictions [0092]; log file predictions consider browsing data ("user behavior data") [0078, 0083]. Prior art not relied upon but considered relevant Ni et al. (U.S. PGPub No 20220308952) discloses words in each sequence (of log segments [0031]) may be masked, where the machine learning model (BERT model) may attempt to predict the original value of the masked words [0074]; the BERT model is pre-trained [0073]. The prior art of record fails to disclose, without the use of impermissible hindsight reasoning [see added emphasis for distinguishing material]: and train the machine learning model to predict next log file names in log file name sequences in response to training the machine learning model to predict masked log file names in the log file name sequences. Claims 12-14 depend upon the allowable subject matter of Claim 11. Claims 19 depends upon the allowable subject matter of Claim 18.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Petkar (U.S. Patent No. 11281521) discloses a method for performing troubleshooting using analysis of log files, where a test platform may generate log files indicating various details regarding a test session and may utilize a log file viewer to show the log file data and root cause information.
Xu et al. (U.S. PGPub No. 20190179691) discloses a model predicting log patterns, where log patterns are matched to log information to detect system failure.
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/A.E.W./Examiner, Art Unit 2113 /BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113
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