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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7,10-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes – concepts performed in the human mind.
Regarding claim 1, with the exception of the limitations ‘by at least one server processor; by the at least one server processor’, the claim is directed to mental processes and mathematical concepts.
The limitations ‘applying one or more sampling techniques to the current tabular log data to form current balanced log data, wherein the current balanced log data includes previous incidents of failures of the at least one software application’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion. The limitation ‘converting the current raw log data into current tabular log data, wherein the current tabular log data is configured into a data structure with a readable tabular format by generating a dictionary and fields that provide additional details regarding the current raw log data’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion with the aid of pen and paper.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional elements ‘by at least one processor; by the at least one processor; wherein the dictionary and fields are stored with the readable tabular format in a virtual environment’; ‘applying one or more machine learning techniques to the current balanced log data to generate an application failure predictive model based at least in part on the previous incidents of failures of the at least one software application’; ‘retraining, by the at least one server processor, the application failure predictive model with the current balanced log data when a predetermined threshold value is reached’ are directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)). The processors are generic computer components. The machine learning techniques are not disclosed at any level of specificity; thus it is a generic component.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘obtaining current raw log data from at least one application log of at least one software application running in a client device; generating at least one indication of at least one failure of the software application running in the client device based on an application failure predictive model applied on the current balanced log data; causing based on the at least one indication, the software application to cease running in the client device in an event of the at least one failure of the software application’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The ‘obtaining’ limitation is data gathering. The ‘generating’ limitation is merely identifying a failure generically and the ‘causing’ limitation is merely generically correcting or avoiding a failure.
Regarding claim 2, the limitation ‘wherein the one or more sampling techniques comprise an undersampling technique’ is a mental process of deciding to use this technique.
Regarding claim 3, the limitation ‘wherein the undersampling technique comprises: parsing the current tabular log data into majority class data and minority class data; reducing the majority class data to a size commensurate with the size of the minority class data; and generating the current balanced log data using the reduced-size majority class data and the minority class data’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion as well as by a human using a pen and paper.
Regarding claim 4, the limitation ‘wherein applying the one or more machine learning techniques to the current balanced log data comprises utilizing logistic regression to determine relationships between at least one dependent variable of the current balanced log data and one or more independent variables of the current balanced log data’ are mathematical concepts.
Regarding claim 5, the limitation ‘wherein the logistic regression comprises a least absolute shrinkage and selection operator (LASSO) logistic regression that performs variable selection and regularization of the current balanced log data’ is a mathematical concept.
Regarding claim 6, the limitation ‘splitting the current balanced log data into training data and testing data, wherein the training data and the testing data are utilized to verify the model’s integrity’ is a mental process of deciding how to use the log data.
Regarding claim 7, the limitation ‘wherein the applying the one or more sampling techniques to the current tabular log data comprises one hot encoding the current tabular log data’ is a mental process of deciding what technique to apply.
Regarding claim 10, the limitation ‘wherein the dictionary comprises a dictionary list and dictionary entries for each element of log data’ is a mental process - concept performed in the human mind by observation, evaluation, judgment, and/or opinion as well as by a human using a pen and paper.
Regarding claim 11, the limitation ‘further comprising, in connection with configuring the tabular data into the readable tabular format: returning a dictionary list; scanning a row of the dictionary list to generate keys that are used as column headers for in the readable tabular format are mental processes - concepts performed in the human mind by observation, evaluation, judgment, and/or opinion as well as by a human using a pen and paper;
writing, to a CSV file, the keys that correspond to the column headers; and writing rows from the dictionary list to fill the CSV file are directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)) .
Regarding claim 12, the limitation ‘further comprising: transforming the current raw log data into the readable tabular format; and generating input date, time, logType, and message fields for each instance of the transformed current raw log data’ is a mental process - concept performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 13, the limitation ‘wherein the dictionary has a dictionary entry for each instance of log data, wherein dictionary entries are comprised of 5 or more of log name, date, time, timestamp, message, category, day of the week, month of the year, success, hour of the day, minute of the hour, and/or day of the month’ is a mental process - concept performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 14, the limitation ‘wherein the dictionary having a dictionary entry for each instance of the tabular log data; and data in the dictionary are generated via looping through every line of the log data and appending information regarding each said dictionary entry into the dictionary’ is a mental process - concept performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 15, the limitation ‘wherein the raw log data is comprised of three log types, including outbound logs, inbound logs, and batch logs’ is a mental process of choosing log types.
Regarding claim 16, the limitations ‘further comprising: updating one or more string names associated with one or more dictionary entries to numerical values, wherein each of the dictionary entries corresponding to an instance of the tabular log data, and the updating comprises: processing a list of dictionary entries and a category variable; looping-through a list of job names, from the one or more string names, to parse out a Business name, a Log Function name, a Business Function name, a Business Process name, and a numerical Log name; looping-through a list of Business names, correlating each said Business name to a number, and returning a first numerical value; looping-through a list of Log Function names, correlating each said Log Function name to a number, and returning a second numerical value; looping-through a list of Business Function names, correlating each said Business Function name to a number, and returning a third numerical value; looping-through a list of Business Process names, correlating each said Business Process name to a number, and returning a fourth numerical value; and creating the numerical Log name via combining two or more of the first numerical value, the second numerical value, the third numerical value, and the fourth numerical value together to form the numerical Log name, wherein each said numerical Log name comprises a complete unique numerical identifier for each string name’ are mental processes - concepts performed in the human mind by observation, evaluation, judgment, and/or opinion well as by a human using a pen and paper.
Regarding claim 17, the limitations ‘wherein generating the dictionary associated with the current tabular log data comprises: processing dictionary list entries comprised of an argument field, an index field, a Dictionary List field, a Log Category field, a Business Name field, a Log Function field, a Business Function field, a Business Process field, and a Log Name field; and updating and adding fields comprising the Log Category field, the Business Name field, the Log Function field, the Business Function field, the Business Process field, and a Log Name to the dictionary, wherein the Log Name is a unique numerical identifier created by combining a plurality of fields into one unique value’ are mental processes - concepts performed in the human mind by observation, evaluation, judgment, and/or opinion well as by a human using a pen and paper.
Regarding claim 18, with the exception of the limitations ‘by at least one server processor; by the at least one server processor’, the claim is directed to mental processes.
The limitations ‘applying one or more sampling techniques to the current tabular log data to form current balanced log data, wherein the current balanced log data includes incidents of failures’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
The limitation ‘converting, by the at least one server processor, the current raw log data into current tabular log data, wherein the current tabular log data is configured into a data structure with a readable tabular format by generating a dictionary and fields that provide additional details regarding the current raw log data’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion with the aid of pen and paper.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional elements ‘by at least one processor; by the at least one processor; wherein the dictionary and fields are stored with the readable tabular format in a virtual environment; retraining the application failure predictive model with the current balanced log data when a predetermined threshold value is reached’ are directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)). The processors are generic computer components. The machine learning techniques are not disclosed at any level of specificity; thus it is a generic component.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘obtaining current raw log data from at least one application log of at least one software application running in a client device; predicting, based on the future balanced log data, at least one future failure of the software application using an application failure predictive model applied on the current balanced log data’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The ‘obtaining’ limitation is data gathering.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘causing the software application to cease running in the client device in an event of the failure incident’ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo.
FR2481831A1 - In the processing systems of the prior art, the errors of the computer were treated by stopping it and restarting it automatically with a save operation to resume work if the stop was caused by a program alarm as opposed to a circuit deactivation failure.
USPN 20090132848 – paragraph 0074 - Furthermore, conventional systems cease executing a parallel program upon detection of an error, whereas parallel try/catch command 600 enables a parallel program to continue execution upon detection of an error. Thus, parallel try/catch command 600 provides error-tolerant parallel code.
Regarding claim 19, with the exception of the limitations ‘by a server processor; by the server processor’, the claim is directed to mental processes and mathematical concepts.
The limitations ‘parsing the application log data and the additional details using one or more sampling techniques to yield parsed log data’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion. The limitation ‘performing logic regressions of the parsed log data to provide scores of the parsed log data’ is a mathematical concept.
The limitation ‘processing the application log data to provide additional details regarding log entries, wherein the processed application log data is configured into a data structure with a readable tabular format by generating a dictionary and fields that provide the additional details regarding the application log data’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion with the aid of pen and paper.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional elements ‘by a server processor; by the server processor; wherein the dictionary and fields are stored with the readable tabular format in a virtual environment; predicting when a failure incident is expected to occur by processing the scores of the parsed log data against an application failure predictive model; retraining the application failure predictive model with the current balanced log data when a predetermined threshold value is reached’ are directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)). The machine learning techniques are not disclosed at any level of specificity; thus it is a generic component.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘collecting application log data from the software application’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The ‘collecting’ limitation is data gathering.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘causing the software application to cease running in the client device in an event of the failure incident’ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo.
FR2481831A1 - In the processing systems of the prior art, the errors of the computer were treated by stopping it and restarting it automatically with a save operation to resume work if the stop was caused by a program alarm as opposed to a circuit deactivation failure.
USPN 20090132848 – paragraph 0074 -Furthermore, conventional systems cease executing a parallel program upon detection of an error, whereas parallel try/catch command 600 enables a parallel program to continue execution upon detection of an error. Thus, parallel try/catch command 600 provides error-tolerant parallel code.
Regarding claim 20, the limitation ‘wherein the one or more sampling techniques comprise an undersampling technique, and the undersampling technique comprises: parsing the tabular data into majority class data and minority class data; reducing the majority class data to a size commensurate with the size of the minority class data; and generating the balanced log data using the reduced-size majority class data and the minority class data’ are mental processes - concepts performed in the human mind by observation, evaluation, judgment, and/or opinion well as by a human using a pen and paper.
Response to Arguments
Applicant's arguments and amendments filed 01/27/2026 have been fully considered. Concerning Applicant’s arguments of the 101 abstract idea rejection, the claims do not fall within any statutory category. The newly added limitation of a data structure is not explained in any degree of specificity as to the type of data structure the data is being formatted for. Therefore, the human mind can perform this by observation, evaluation, judgment, and/or opinion with the aid of pen and paper. The Examiner fails to see any improvements to computer capabilities. The claims are merely directed to data gathering, converting the data into a readable format, applying a sampling technique, identifying an error by a machine learning model, closing the application that has an error, then retraining the model based on some sort of threshold value being reached. There is no indication of what threshold value is being reached that is causing the retraining of the machine learning model. The computer components are generic that are used to perform the steps of the claims and are merely automating human thought processes by using a computer as a tool.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Yolanda L Wilson whose telephone number is (571)272-3653. The examiner can normally be reached M-F (7:30 am - 4 pm).
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, Bryce Bonzo can be reached on 571-272-3655. 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.
/Yolanda L Wilson/Primary Examiner, Art Unit 2113
Prosecution Insights