CTNF 18/658,413 CTNF 101481 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION This action is in response to application filed 05/08/2024. Claims 1-20 are pending. Specification 07-29 AIA The disclosure is objected to because of the following informalities: In paragraph [0010], the specification states “script skep demographics” where instead it should say “script skip demographics” . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Regarding claims 8 and 19, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). Examiner interprets the capture details after “such as” as mere examples and not part of the claim. Examiner recommends amending claim language to clearly claim the intent of the invention. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15-aia AIA Claim(s) 1-2, 6-8, 12-13, and 17-20 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Kocura et al. (US 10162741 B2) hereinafter Kochura . Regarding claim 1, Kochura discloses A computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and at least one of memory storing computer-readable instruction that, when executed by the at least one processor, configure the computing platform to: (Kochura column 15, lines 62-67 and column 16, lines 1-8 and abstract discloses data system comprising processor, memory, and storing/executing code to memory elements through a communication bus). train, based on historical data related to a plurality of test automation script interruptions, a machine learning model to identify, based on current test automation script interruption data, corrective actions for resolving interruptions to test automation scripts; (Kochura Column 9, lines 47-67 discloses the training data consists of a combination of corrections and retraining the application to recognize changes in the GUI. The data used is not only the present properties, but also the previous properties it changed from, i.e. the historical data). detect, based on monitoring at least one computing device executing a first test automation script, an interruption in the first test automation script; (Kochura column 12, lines 27-38 discloses that when a tester executes a script to perform GUI automation functions in GUI for testing of application. When a GUI objected cannot be identified in GUI, then a failure occurs). identify data associated with the interruption, the data including at least identification of a point in the first test automation script at which the interruption occurred; (Kochura figure 11 and column 13, lines 25-46 discloses that while running the script the performs GUI automation functions, the mechanism determines whether a GUI automation fails because an expected GUI object is not found. At this point, the ML model can identify and correct the GUI control in the script after determining that the ML can correct this issue, shown in figures 1103-1105. Finally, in figure 1106 the script is corrected and returned back to the point of interruption, figure 1101 and continues running the script, thus demonstrating halting/resuming at the point of interruption and identifying/correcting the issue at a specified point in the automation script) determine, based on the data associated with the interruption, whether the interruption can be processed by the computing platform; (Kochura column 12, lines 39-50 discloses the automation tool comparing confidence value for the candidate GUI control object to a threshold to determine if the automation tool can process the error or not). responsive to determining that the interruption cannot be processed by the computing platform, transfer the data associated with the interruption to an administrator computing device for processing; (Kochura column 12, lines 39-50 discloses that if the confidence value is not greater than the threshold, then the GUI automation tool prompts the tester to select the identified GUI object or another GUI object in GUI). responsive to determining that the interruption can be processed by the computing platform: execute the machine learning model, wherein executing the machine learning model includes inputting, to the machine learning model, the data associated with the interruption to output a corrective action for the interruption; execute the corrective action; (Kochura column 12, lines 39-50 discloses that if the confidence value is greater than the threshold, then the automation tool automatically performs the GUI automation function on the identified GUI object, correcting the GUI control in script). after executing the corrective action, causing testing of the first test automation script to resume from the point in the first test automation script at which the interruption occurred; (Kochura column 13, lines 7-12 discloses that if the automation tool did not fail implementing the corrective actions, then operation returns to continue execution of the script) record a plurality of information of the interruption and the executed corrective action in a resumption log; and update the machine learning model based on the executed corrective action. (Kochura column 11, lines 64-67 and column 12, lines 1-3 and 39-56 discloses after correcting the script, the automation tool may record the correction in a corrections data structure to be fed back into the ML model or used later for further training of ML model. The corrections in the corrections data structure consists of the scripts previous properties and the new properties of the identified object. Thus, the corrections consist of both the previous properties/interruption information, and the current properties. Further in column 13, lines 13-24, discloses training a machine learning model using the “ground truth” which is the from the recorded corrections gathered). Regarding claim 2, Kochura discloses The computing platform of claim 1, wherein transferring the data associated with the interruption to an administrator computing device for processing further includes: sending a correction notification to the administrator computing device, wherein sending the correction notification to the administrator computing device causes the administrator computing device to display the notification. (Kochura column 12, lines 39-50 and figure 8 discloses that the tester is a computing device in element 801 of the figure, and the automation tool prompts the tester when the tool determines that it cannot confidently resolve the failure automatically. Further, in Kochura claim 4, correcting the script comprises prompting a user to approve the candidate user interface object, thus demonstrating that the prompt is sent to the admin computer and displays to the admin). Regarding claim 6, Kochura discloses The computing platform of claim 1, wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: identify, based on an analysis from the machine learning model, a cause of the interruption. (Kochure column 12, lines 27-38 discloses that the cause for the failure/interruption is when the GUI object in the script cannot be identified in the GUI, thus demonstrating the unidentified object as the cause for interruption). Regarding claim 7, Kochura discloses The computing platform of claim 1, wherein determining whether the interruption can be processed by the computing platform includes analyzing the resumption log to determine whether the resumption log includes interruptions having one of: a type or cause similar to a type or cause of the interruption. (Kochura column 12, lines 16-39 discloses that the model uses actual corrections to known objects and labels those as true, while unknown objects are labeled as false. The classification model then uses the classification model to train the tool using clustering to divide the GUI objects into clusters of similar objects, thus training the model to find GUI objects that are similar to GUI objects in corrections. This demonstrates the correction dataset being similar to the resumption log and the correction dataset consisting of previous interruptions/previous corrections and groups them on similarity to then use for future corrections). Regarding claim 8, Kochura discloses The computing platform of claim 1, wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: capture details of a resumption such as run identification, execution cycle number, iteration number, script skip demographics, and script resumption demographics and records the details in the resumption log. (Kochura Column 13, lines 47-55 discloses recording correction of the GUI control in a correction data structure and corrects the GUI control in the script. As [0010] of the specification discloses, resumption is merely the corrective action and output stored in the resumption log. See 112(b) rejection above.) With regards to claim 12, it is a method claim having similar limitations cited in claim 1. Thus, claim 12 is also rejected under the same rationale as cited in the rejection of claim 1 above. With regards to claim 13, it is a method claim having similar limitations cited in claim 2. Thus, claim 13 is also rejected under the same rationale as cited in the rejection of claim 2 above. With regards to claim 17, it is a method claim having similar limitations cited in claim 6. Thus, claim 17 is also rejected under the same rationale as cited in the rejection of claim 6 above. With regards to claim 18, it is a method claim having similar limitations cited in claim 7. Thus, claim 18 is also rejected under the same rationale as cited in the rejection of claim 7 above. With regards to claim 19, it is a method claim having similar limitations cited in claim 8. Thus, claim 19 is also rejected under the same rationale as cited in the rejection of claim 8 above. With regards to claim 20, it is a non-transitory medium claim having similar limitations cited in claim 1. Thus, claim 20 is also rejected under the same rationale as cited in the rejection of claim 1 above . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 3, 9, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kochura et al. (US 10162741 B2) hereinafter Kochura in view of Emery et al. (US 20210209094 A1) hereinafter Emery . Regarding claim 3, Kochura discloses The computing platform of claim 1 Kochura lacks explicitly wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to format the information of the interruption; and produce a formatted input entry based on the information of the interruption. Emery teaches wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to format the information of the interruption; and produce a formatted input entry based on the information of the interruption. (Emery [0130] discloses a failure during device use the information needs to be updated and the updated information needs to be transmitted to the distributed ledger of the blockchain in a transaction format, demonstrating that the failure/interruption is being put in a particular format and transmitted/inputted). It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to have modified Kochura to incorporate the teachings of Emery to “wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to format the information of the interruption; and produce a formatted input entry based on the information of the interruption” in order to have a standardized format allowing the machine learning model to be able to parse through interruption data, improving the model’s efficiency/accuracy. Regarding claim 3, Kochura discloses The computing platform of claim 1 Kochura lacks explicitly wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: store data in a distributed ledger. Emery teaches wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: store data in a distributed ledger. (Emery [0130] discloses the data being sent/stored in a distributed ledger of the blockchain). It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to have modified Kochura to incorporate the teachings of Emery to “wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: store data in a distributed ledger” in order to have data decentralized, thus increasing the security of the machine learning model and information associated as it will not be inoperable due to a single point of failure from more centralized datastores. With regards to claim 14, it is a method claim having similar limitations cited in claim 3. Thus, claim 14 is also rejected under the same rationale as cited in the rejection of claim 3 above . 07-21-aia AIA Claim (s) 4-5, 10, and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kochura et al. (US 10162741 B2) hereinafter Kochura in view of Hanke et al. (US 10871977 B2) hereinafter Hanke . Regarding claim 4, Kochura discloses The computing platform of claim 1, and responsive to determining that executing the corrective action a second time did not resolve the interruption, send a correction notification requesting user input to the administrator computing device, wherein sending the notification causes the administrator computing device to display the correction notification. Kochura lacks explicitly wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: determine that the executed corrective action did not resolve the interruption; responsive to determining that the executed corrective action did not resolve the interruption, execute the corrective action a second time; Hanke teaches wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: determine that the executed corrective action did not resolve the interruption (Hanke Column 6, lines 13-17 discloses that the modified script resulted in another error). responsive to determining that the executed corrective action did not resolve the interruption, execute the corrective action a second time; (Hanke Column 6, lines 5-31 discloses continuing to run the test automation script based on the modified script and if it results in another error relating to the replacement UI object, the method may iterate for a second time by invoking the self-helping tool again in order to find another fix/replacement. While Hanke does not disclose explicitly notifying an administrator after a second failure, with the combination of Kochura teaching prompting a tester when the machine learning model is not confident in apply corrections, it would be obvious to combine Kochura and Hanke to teach this limitation of contacting an admin after repeated correction failure). It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to have modified Kochura to incorporate the teachings of Hanke to “wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: determine that the executed corrective action did not resolve the interruption and responsive to determining that the executed corrective action did not resolve the interruption, execute the corrective action a second time” in order correct an issue in the script that the model cannot, thus improving accuracy while avoiding further interrupts in the script. Regarding claim 5, Kochura discloses The computing platform of claim 1 Kochura lacks explicitly wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: identify, based on the information of the interruption, a type of the interruption. Hanke teaches wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: identify, based on the information of the interruption, a type of the interruption. (Hanke Column 4, lines 10-37 discloses when the automation script “breaks” or interrupted due to a failure, the self-healing takes control by first capturing the former properties of the missing object. Then the tool captures data associated with all current objects having the same object “type” as the missing objects, thus showing that the tool is getting information of the type of interruption that occurred). It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to have modified Kochura to incorporate the teachings of Hanke to “wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: identify, based on the information of the interruption, a type of the interruption” in order to quickly identify a type of error, which will allow the machine learning model to be able to find similar historical issues revolving that type of error, improving the speed and accuracy of corrections applied to the script. Regarding claim 10, Kochura discloses The computing platform of claim 1 Kochura lacks explicitly wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: generate a similarity score based on a degree of similarity between the interruption and historical interruptions stored in the resumption log. Hanke teaches wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: generate a similarity score based on a degree of similarity between the interruption and historical interruptions stored in the resumption log. (Hanke column 1, lines 49-67 and column 2, lines 1-4 discloses remediation of software scripts attempting to identify a first UI object and in response to failing to identify first UI object, similarity scores are calculated based on data of a plurality of UI objects and historical data associated with the first UI object. Further, Hanke column 3, lines 15-30 show that the historical objects used for the similarity score from a database of historical objects). It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to have modified Kochura to incorporate the teachings of Hanke to “wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: generate a similarity score based on a degree of similarity between the interruption and historical interruptions stored in the resumption log” in order to have the ML model able to quickly determine if a past correction of the interruption is in the log, allowing a fast resolution to an issue that has been already solved previously. With regards to claim 15, it is a method claim having similar limitations cited in claim 4. Thus, claim 15 is also rejected under the same rationale as cited in the rejection of claim 4 above. With regards to claim 16, it is a method claim having similar limitations cited in claim 5. Thus, claim 16 is also rejected under the same rationale as cited in the rejection of claim 5 above . 07-21-aia AIA Claim (s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kochura et al. (US 10162741 B2) hereinafter Kochura in view of Keskin et al. (US 20240427683 A1) hereinafter Keskin . Regarding claim 11, Kochura discloses The computing platform of claim 1 Kochura lacks explicitly wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: analyze the resumption log; and store a first gained intelligence, based on the analysis of the resumption log, into an intelligence database. Keskin teaches wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: analyze the resumption log; and store a first gained intelligence, based on the analysis of the resumption log, into an intelligence database. (Keskin [0058] discloses reports of performance data in the form of a log file, where the performance data may be descriptive of events associated with an interrupt. Then, on [0061] the log files are analyzed and classified. Finally, on [0010], the analysis of these log files is stored in the database to train for future event dependencies). It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to have modified Kochura to incorporate the teachings of Keskin to “wherein the computing platform stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: analyze the resumption log; and store a first gained intelligence, based on the analysis of the resumption log, into an intelligence database” in order to have a database of corrective measures, allowing the machine learning model to identify issues and the fixes for those issues efficiently. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER J SALLEY whose telephone number is (571)272-6355. The examiner can normally be reached Mon-Fri, 7:30am-5pm. 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, Chat Do can be reached at (571) 272-3721. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CHRISTOPHER J SALLEY/Examiner, Art Unit 2193 /Chat C Do/Supervisory Patent Examiner, Art Unit 2193 Application/Control Number: 18/658,413 Page 2 Art Unit: 2193 Application/Control Number: 18/658,413 Page 3 Art Unit: 2193 Application/Control Number: 18/658,413 Page 4 Art Unit: 2193 Application/Control Number: 18/658,413 Page 5 Art Unit: 2193 Application/Control Number: 18/658,413 Page 6 Art Unit: 2193 Application/Control Number: 18/658,413 Page 7 Art Unit: 2193 Application/Control Number: 18/658,413 Page 8 Art Unit: 2193 Application/Control Number: 18/658,413 Page 9 Art Unit: 2193 Application/Control Number: 18/658,413 Page 10 Art Unit: 2193 Application/Control Number: 18/658,413 Page 11 Art Unit: 2193 Application/Control Number: 18/658,413 Page 12 Art Unit: 2193 Application/Control Number: 18/658,413 Page 13 Art Unit: 2193 Application/Control Number: 18/658,413 Page 14 Art Unit: 2193
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